{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial notebook 8: Multi-cell tissue prediction\n",
    "\n",
    "In this tutorial, we will demonstrate how to finetune a Cell2Sentence model on multi-cell tasks. Single-cell analysis typically requires looking at many cells at once, so it can be useful to have a model that can reason about multiple cells at a time. LLMs provide flexibility in input prompt formatting, so we can easily modify the input prompt of Cell2Sentence to encode multiple cells in a single input.\n",
    "\n",
    "In this tutorial, we will finetune a Cell2Sentence model on a multi-cell tissue prediction task. We will use the same dataset and model as in the previous tutorials, however each input sample will now contain multiple cells, and we will train on an example task of predicting the tissue type of a group of cells based on their gene expression.\n",
    "\n",
    "At a high level, we will:\n",
    "1. Construct an example multi-cell dataset by grouping cells together based on batch sample and tissue type.\n",
    "2. Use a multi-cell prompt formatter to format our dataset for the multi-cell tissue prediction task.\n",
    "3. Finetune a Cell2Sentence model on our multi-cell task.\n",
    "\n",
    "We will use the same dataset and base C2S model as in the previous tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python built-in libraries\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" \n",
    "os.environ[\"WORLD_SIZE\"] = \"1\"\n",
    "\n",
    "import random\n",
    "from datetime import datetime\n",
    "\n",
    "# Third-party libraries\n",
    "from datasets import Dataset, concatenate_datasets\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from transformers import TrainingArguments\n",
    "\n",
    "# Single-cell libraries\n",
    "import anndata\n",
    "import scanpy as sc\n",
    "\n",
    "# Cell2Sentence imports\n",
    "import cell2sentence as cs\n",
    "from cell2sentence.prompt_formatter import C2SMultiCellPromptFormatter\n",
    "from cell2sentence.utils import train_test_split_arrow_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n",
    "\n",
    "Next, we will load the preprocessed dataset from the tutorial 0. This dataset has already been filtered and normalized, so it it ready for transformation into cell sentences.\n",
    "\n",
    "<font color='red'>Please make sure you have completed the preprocessing steps in Tutorial 0 before running the following code, if you are using your own dataset.</font>. Ensure that the file path is correctly set in <font color='gold'>DATA_PATH</font> to where your preprocessed data was saved from tutorial 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = \"/home/sr2464/scratch/C2S_Files/Cell2Sentence_Datasets/dominguez_conde_immune_tissue_two_donors_preprocessed_tutorial_0.h5ad\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 29773 × 23944\n",
       "    obs: 'cell_type', 'tissue', 'batch_condition', 'organism', 'assay', 'sex', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt'\n",
       "    var: 'gene_name', 'ensembl_id', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'\n",
       "    uns: 'batch_condition_colors', 'cell_type_colors', 'log1p', 'neighbors', 'pca', 'tissue_colors', 'umap'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    varm: 'PCs'\n",
       "    obsp: 'connectivities', 'distances'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = anndata.read_h5ad(DATA_PATH)\n",
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs = adata.obs[[\"cell_type\", \"tissue\", \"batch_condition\", \"organism\", \"sex\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "      <th>tissue</th>\n",
       "      <th>batch_condition</th>\n",
       "      <th>organism</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAACCTGCAAATTGCC</th>\n",
       "      <td>CD4-positive helper T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAACGGGCATCTGGTA</th>\n",
       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAACGGGTCTTGCATT</th>\n",
       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAAGCAATCATCGCTC</th>\n",
       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAAGTAGCAGTCACTA</th>\n",
       "      <td>gamma-delta T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            cell_type tissue  \\\n",
       "Pan_T7935490_AAACCTGCAAATTGCC              CD4-positive helper T cell  ileum   \n",
       "Pan_T7935490_AAACGGGCATCTGGTA  CD8-positive, alpha-beta memory T cell  ileum   \n",
       "Pan_T7935490_AAACGGGTCTTGCATT  CD8-positive, alpha-beta memory T cell  ileum   \n",
       "Pan_T7935490_AAAGCAATCATCGCTC  CD8-positive, alpha-beta memory T cell  ileum   \n",
       "Pan_T7935490_AAAGTAGCAGTCACTA                      gamma-delta T cell  ileum   \n",
       "\n",
       "                              batch_condition      organism     sex  \n",
       "Pan_T7935490_AAACCTGCAAATTGCC             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAACGGGCATCTGGTA             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAACGGGTCTTGCATT             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAAGCAATCATCGCTC             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAAGTAGCAGTCACTA             A29  Homo sapiens  female  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_name</th>\n",
       "      <th>ensembl_id</th>\n",
       "      <th>n_cells</th>\n",
       "      <th>mt</th>\n",
       "      <th>n_cells_by_counts</th>\n",
       "      <th>mean_counts</th>\n",
       "      <th>pct_dropout_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RP11-34P13</th>\n",
       "      <td>RP11-34P13</td>\n",
       "      <td>ENSG00000238009</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>38</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>99.872368</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP11-34P13-3</th>\n",
       "      <td>RP11-34P13</td>\n",
       "      <td>ENSG00000241860</td>\n",
       "      <td>106</td>\n",
       "      <td>False</td>\n",
       "      <td>106</td>\n",
       "      <td>0.003627</td>\n",
       "      <td>99.643973</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP006222</th>\n",
       "      <td>AP006222</td>\n",
       "      <td>ENSG00000286448</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>99.976489</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01409</th>\n",
       "      <td>LINC01409</td>\n",
       "      <td>ENSG00000237491</td>\n",
       "      <td>1292</td>\n",
       "      <td>False</td>\n",
       "      <td>1292</td>\n",
       "      <td>0.045981</td>\n",
       "      <td>95.660498</td>\n",
       "      <td>1369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM87B</th>\n",
       "      <td>FAM87B</td>\n",
       "      <td>ENSG00000177757</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>99.989924</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               gene_name       ensembl_id  n_cells     mt  n_cells_by_counts  \\\n",
       "RP11-34P13    RP11-34P13  ENSG00000238009       38  False                 38   \n",
       "RP11-34P13-3  RP11-34P13  ENSG00000241860      106  False                106   \n",
       "AP006222        AP006222  ENSG00000286448        7  False                  7   \n",
       "LINC01409      LINC01409  ENSG00000237491     1292  False               1292   \n",
       "FAM87B            FAM87B  ENSG00000177757        3  False                  3   \n",
       "\n",
       "              mean_counts  pct_dropout_by_counts  total_counts  \n",
       "RP11-34P13       0.001310              99.872368          39.0  \n",
       "RP11-34P13-3     0.003627              99.643973         108.0  \n",
       "AP006222         0.000235              99.976489           7.0  \n",
       "LINC01409        0.045981              95.660498        1369.0  \n",
       "FAM87B           0.000101              99.989924           3.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ap2853/.conda/envs/scFB2/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(\n",
    "    adata,\n",
    "    color=\"cell_type\",\n",
    "    size=8,\n",
    "    title=\"Human Immune Tissue UMAP\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.408124"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are expecting log10 base 10 transformed data, with a maximum value somewhere around 3 or 4. Make sure to start with processed and normalized data when doing the cell sentence conversion!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell2Sentence Conversion\n",
    "\n",
    "In this section, we will transform our AnnData object containing our single-cell dataset into a Cell2Sentence (C2S) dataset by calling the functions of the CSData class in the C2S code base. Full documentation for the functions of the CSData class can be found in the documentation page of C2S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cell_type', 'tissue', 'batch_condition', 'organism', 'sex']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_obs_cols_to_keep = adata.obs.columns.tolist()\n",
    "adata_obs_cols_to_keep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29773/29773 [00:10<00:00, 2973.20it/s]\n"
     ]
    }
   ],
   "source": [
    "# Create CSData object\n",
    "arrow_ds, vocabulary = cs.CSData.adata_to_arrow(\n",
    "    adata=adata, \n",
    "    random_state=SEED, \n",
    "    sentence_delimiter=' ',\n",
    "    label_col_names=adata_obs_cols_to_keep\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['cell_name', 'cell_sentence', 'cell_type', 'tissue', 'batch_condition', 'organism', 'sex'],\n",
       "    num_rows: 29773\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrow_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cell_name': 'Pan_T7935490_AAACCTGCAAATTGCC',\n",
       " 'cell_sentence': 'RPLP1 ACTB EEF1A1 HSP90AA1 TMSB4X B2M FTH1 KLF6 HSPA1B MALAT1 RPS12 HSPA8 RPL13 MT-CO1 ATF3 MT-CO2 RPL41 TPT1 MT-CO3 RPS19 HLA-B RPL10 RPS4X RPL28 MT-CYB DUSP1 RPL30 MT-ND4L RPS15 FOS RPL34 RPS2 RPLP2 MT-ND3 RPS18 RPS8 TRBV7-2 RPL32 RPS3 ANXA1 RPL11 HLA-C RPS27 ACTG1 UBC RPL3 RPL37 RPLP0 MT-ATP6 JUNB RPS28 RPL18 UBB MT-ATP8 RPS14 RPL39 PFN1 GAPDH HSPA1A RPL18A SRGN RPS27A RPL26 RPL19 RPS15A HLA-A DNAJB1 RPS3A CREM RPS13 MT-ND1 RPL21 RPS25 BTG2 RPL35A FAU RPL8 RPL7A RPS24 RPS6 RPS16 RACK1 NFKBIA RGS1 RPL29 CALM1 RPL9 RPL37A MT-ND5 TNFAIP3 RPS23 IL7R RPL36A PTMA NFKBIZ UBA52 EIF1 CRIP1 CORO1A RPL14 HSP90AB1 RPL10A CXCR4 RPL4 EEF1B2 RPL36 RPS9 RPL27 NACA VIM H3-3B RPS7 HSPH1 ATP5F1E HLA-E RPL17 RPSA MYL12A RPL12 CD69 TAGAP RPL35 RPS29 RPL6 SARAF ZFP36L2 MT-ND4 ARHGDIB BTG1 RPS21 EEF1D PNRC1 EEF1G HSPA5 FYB1 CD3E IFITM1 RNASEK EEF2 MT-ND2 FTL S100A4 JUN IFITM2 CYTIP OST4 LAPTM5 RPL36AL PLAAT4 PFDN5 SAMSN1 DNAJA1 EIF4A1 FXYD5 HSPE1 CTLA4 IL32 RPL24 CFL1 SAT1 RPL13A NPM1 NDUFV2 SRSF7 ITM2B POLR1D CCNI CALR ZFP36 COX7C PTPRC GADD45A CD3G RAC2 MYL6 UBE2D3 CCL20 RPS5 RPL22 TOMM7 RPL15 C12ORF57 LSP1 SON H4C3 RPL23A RPL31 DDX5 EVL AHR SERF2 STK17A CD6 PTGER4 OSTF1 OAZ1 TMA7 PHLDA1 COMMD6 PPIA TMSB10 RHOH TUBA1B KTN1 PDIA3 DUSP5 BTF3 SSR4 ATP5MC2 S100A6 CEBPD TRAV13-2 STAT1 HSPB1 PSMB9 RPL38 HNRNPA2B1 FLT3LG CSTB CDK2AP2 CSK MIF MCL1 PSME1 TXNIP RHOA HNRNPA1 UBL5 PTGES3 PDCD4 SLC2A3 ERCC1 SNHG29 NFKBID SRP14 ARPC1B NR4A2 CMTM6 CD44 ITGA4 GNB2 TRAV19 BHLHE40 HMGB1 ARPC3 TRIR PRKCH NME2 KLRB1 CLIC1 ANAPC16 NCOR1 COX5B MYL12B SAP18 RHOB TCF25 SOD1 SRSF9 FOSB PCBP1 CD96 TBC1D10C CD164 CD3D ACTR2 COX4I1 SH3BGRL3 KMT2E HSPD1 PABPC1 SPCS2 SERP1 TNFRSF1B TAGLN2 PCBP2 RPL5 LDLR YWHAB CSRNP1 COX8A MYADM TRMT112 ATP5MG GABARAP ELF1 PRR13 RPL22L1 WAKMAR2 SIT1 GADD45B RPS17 S100A10 RBBP4 HNRNPDL CD2 UQCRQ COX6B1 RGS2 CHCHD2 TAF1B RPL27A TAP1 MTDH CD99 ARF1 TBCA TMEM14B HNRNPA0 RPS11 SEC61A1 RSRP1 TUBB ACIN1 HNRNPUL1 OXNAD1 LMO4 CTSH RBM8A RGL4 NFU1 AP1G2 TSC22D3 CASS4 CCNH RPS26 PIK3R1 SH3KBP1 SSBP4 TPR GADD45G ZFAS1 COTL1 BCL7C MZT2A LAT FUS SDF2L1 KRTCAP2 MRPL41 CENPX SETD5 TMC8 COX17 DNAJB4 CDKN1B ODC1 ACAP1 ANXA6 CYBA SUMO3 TMBIM6 TMEM259 CD37 PRDX2 UBE2D2 BTG3 PARK7 CD247 TMEM258 HNRNPAB SPOCK2 SEPTIN1 LAMTOR5 RNF11 SNX5 SASH3 PPM1G ARF6 EIF5A IL4I1 RBM3 RSF1 SLA CALM3 SNRPB SHISA5 ARID5B KXD1 PTPRCAP GBP1 STN1 NSA2 ATP1A1 LCK PSMB8 PSMB3 HNRNPC RNF167 RESF1 ACTR3 CKLF CD52 RPS27L GSTK1 GIMAP7 DCXR RGS14 VASP PBXIP1 GOLGA4 RPL23 YWHAQ SNHG16 TAOK3 IGBP1 DOCK10 SRRM1 PSMC3 IKZF3 AHI1 RPL7 TMEM87A EIF5 MED1 ANKRD12 IWS1 SLC25A6 TUBA1C LRRC41 SEC11C DLST VMA21 MKNK2 RALB THUMPD1 DBF4 RHEB WASHC3 VRK2 SUPT4H1 FADD CHD1 CAPN1 TMEM179B CKS2 RGS10 EIF4E BPTF RNMT DDX39A GADD45GIP1 DYNLL1 SF3B2 CDKN2AIP RAP1B ACTN4 ABRACL MOB4 METTL23 ITSN2 MTF2 VSIR PER1 COX7A2 COX14 IPCEF1 YWHAH CYRIB MOB3A SP2 BCL11B STUB1 RNF213 PRKRA EDF1 PRPF38B RHOG BAG1 ATOX1 CACYBP MAP4K1 PPP1R2 PAXX ADA NDUFA12 WAC ELK3 PRRC2C MUS81 CCT4 ARL5B RALBP1 FBXW11 LCP1 LEPROTL1 PDCD10 HNRNPR NR3C1 PPP4C BATF ABLIM1 PSMA2 TRAT1 RUNX3 CIB1 ARPC2 ABI3 UGP2 NDUFAB1 HCLS1 DERL1 RAB5A INSIG1 CNBP CHROMR DSTYK DPP4 TOMM6 PDE4D LUC7L2 JUND ARHGDIA CEMIP2 POLE4 ANXA11 PLEKHB2 SDCBP MANF FAM83D IMP3 TMCO1 SUCLG1 MBD2 NDUFA10 PDIA6 S100A11 DNPH1 TARDBP PTDSS1 RP11-25K19 LPXN FYTTD1 RORA NASP ATP6V1F RIPK2 IDI1 RASSF7 MT-ND6 RWDD1 CYB5R3 MAZ PIM1 PSMA3 GPX4 PACS1 TNIP1 PLEKHF1 STXBP2 MSMO1 UQCR11 EPB41L4A-AS1 TMIGD2 PATL1 COX6A1 CCND3 TUBB4B OCIAD2 UBE2V2 ASMTL MAP3K8 FERMT3 SH2B1 ACP5 RPS6KB2 GABPB1-AS1 MTRNR2L12 ZFAND2A EIF3D ATP5PB NEDD8 NDUFA1 C19ORF25 CYB561D2_ENSG00000114395 PPP1R18 SYF2 PIGT SLC25A3 KHDRBS1 GIMAP4 NAXE HMGN1 RGS19 SUMO1 YWHAZ ERN1 LRRFIP1 NCF1 GALNT11 PSMG2 SNRPA1 IFNGR1 ENSA LAMTOR4 TBRG1 LINC-PINT PSMA7 SH2D2A HCST GUSB SEC61B TAX1BP1 RNF149 GNG5 PIP5K1A SIGIRR ATP6V1H RXYLT1 TRAPPC2B STK4 GPR174 CLEC2B CDC5L MLEC SQSTM1 GNAS DAXX ADAR NRIP1 TPI1 ITM2C NOSIP HADHA GTF2B ATP5IF1 ATP6V0B VEZF1 MYBL1 TOMM5 UQCRFS1 PDE4B ELL MRPL18 TRAC PIGBOS1 PPIB MDH2 PMVK PGAM1 PEX2 RPA2 FBXW5 MICOS13 CYBC1 ELOB PSMB4 COX6C GTF2I CHMP4A ALOX5AP SSR3 SELENOF AIP UXT TBCB SKAP1 RBM4 NDUFA9 DNAJB11 ATP5MK REX1BD GATA3 MVP GIMAP5 DNAJC2 SHKBP1 CAP1 SSBP1 SUZ12 SNHG6 LTB HECA WDR83OS DUT ANXA2 RRAGA PPP1R12A TAPBP DOK2 DYNLT3 RBPJ ATP6V1G1 CD40LG OCIAD1 TCEA1 ATM SLC44A2 OGA HNRNPK PPP2R5E NDUFA4 PLAUR NT5C3A RBL2 PHTF2 ZFAND5 CCR6 BCL3 GYPC SPTY2D1 MBP AKAP9 CD83 HNRNPF RAC1 SSR2 SMARCC2 MRPS21 SRSF3 DDX21 NDUFC1 BST2 FSD1 TRIB2 SF3B6 GDI2 HLTF APBB1IP LY6E NDUFB4 NAA38 FKBP1A PSMB10 FDPS MBNL1 UQCRB TMED2 H1-10 RO60 CEP57 CDKL3 CAPNS1 GIMAP1 EZR HOXB4 JAGN1 EIF2S2 DNAJB9 KCTD20 ARGLU1 EWSR1 SH3GL1 EIF3F RABGGTA MEA1 MLLT3 SARNP UBAC2 TRA2A ARL6IP1 GPI GUK1 DPH3 TGFBR2 ASB2 SRP9 GNAI2 RAB5IF PPIH EIF4A2 UBQLN1 GPR65 RASAL3 NDUFB6 RPN1 HNRNPA3 GBF1 OGG1 LCP2 DESI2 DUSP2 MGST3 CCDC18-AS1 CD53 POLM RTRAF ME2 PHAX ATP6AP1 RDH11 TCERG1 ACTR8 SLC1A5 DSE SLC25A39 FDFT1 CIAO2A LATS1 WDR33 CCDC107 MARCHF7 FAM174C LRPAP1 COG6 IL10RA MNAT1 CLIC3 TUSC2 NAT9 FAM13B EPG5 FKBP11 CREB3 ARHGAP45 MRPL9 MGA MIDEAS PHF20 CHD9 RCE1 VDAC2 EEF1E1 SSB GBP5 NMRK1 CCT8 SCAP ACER3 VAMP8 TALDO1 PHGR1 TC2N GRID2IP EIF2AK1 TOLLIP TBC1D10A C17ORF100 NFRKB CCDC137 E4F1 XRCC5 FCMR PGPEP1 DGCR6L CHURC1 OFD1 MAP4 PLEKHA2 AMD1 MORF4L1 IER5 DCTN2 CTDSP1 DDX6 AHCYL1 PNKD ELOVL1 FXR1 SAPCD1 SELENOT UBE2L6 PMM2 RNF113A PGRMC2 CPSF6 ZNF267 MRPL50 IL16 CREBL2 ASXL2 NUDCD3 ZNF445 C3AR1 CCR5 DGKE RUNX1 AKAP11 RP11-562I5 ARIH1 PPP1CB NDUFAF3 MRPS5 ACTR1B TPD52 SF3A2 CLTC TMEM33 NDUFA5 HMGXB4 PPP1R21 RC3H2 ZNF580 PRKCSH GZMM SEC13 CTNNA1 VPS18 SYNGAP1-AS1 MAPRE1 ZC3H8 DSTN MAN2B2 MGAT4A PAK2 CRY1 LSM7 ZC3H6 TMEM230 CPQ PSMD3 TIMM17A TYMP ST6GALNAC6 PHB2 KDELR2 LAMP2 NUMB RALY ADAM8 R3HDM4 DNAAF2 ADPRM CCSER2 THRAP3 KIDINS220 RBM15B DEF6 ADNP POLR1E IQGAP1 POFUT2 EIF3K NOL7 NMUR1 NOM1 GNPDA1 PKM CWC25 IGF2R ATP2B4 GPS1 LRP10 CA10 ERI3 SMAD3 ANP32B COX7A2L EHBP1 CHUK H2AZ1 LPIN2 ASAH1 MRPL33 BCL2 DNAJC1 USO1 SUMF2 CHCHD3 NAA20 TMEM60 IER3IP1 FLII YBX1 LAIR1 MESD TSC22D1 SF1 TMEM273 PLCG1 NMD3 TMEM18 ABHD14A SLFN5 ENTR1 CHMP6 IRF2BP2 CHCHD5 HTATSF1 RPS10 LSM14A SUPT7L PEX26 YJU2 HDDC3 CFDP1 SELPLG ENO1 KDM5A MTLN ZNF706 ERVK3-1 DCP1A DBNDD2 TAX1BP3 SPINK4 RNF31 NEPRO SHFL PSMD8 IL17RA CHST14 MPC1 GLOD4 BICRAL SMIM26 KIF5C PRKAR1A TENT5C NPRL2 TTC9C RPUSD3 SSH1 ARHGAP35 RSBN1 ECHDC2 G3BP2 PRR5 SMG9 SUPT3H ATP2A3 SIN3B LSM12 PTEN ZNF93 NONO SRRM2 ITFG1 C21ORF91 HINT1 GOT1 C1QTNF1 AZIN1 MZT1 LAP3 NDUFB8 NDUFA13 FRMD4B LYRM2 IL21R PARP12 SCMH1 PBRM1 HAUS4 RAB4B FLJ40194 HIKESHI NR1D1 MFSD8 SRSF1 SMIM30 XRN2 SAMD4B SEM1 CAPZB PIAS4 IKZF1 EIF3I AGTPBP1 MZT2B C1ORF122 MUTYH WDR61 DDX39B CCNDBP1 ZNF816 PFDN2 POLR2F ANO6 NDUFS6 CTNS ATP11B SIVA1 EIF4E3 CTSC SMC4 CDC42SE2 FAM131C PLPP6 TRPC4AP MTMR14 GABARAPL1 FAM133B RFNG EIF2B1 LSM1 ISY1 EIF3G ORAI2 FAM53B SEPTIN2 HTRA2 SEPTIN6 KAT7 MBD4 CAPZA2 WDR20 NDUFB7 TWF2 COMMD5 GPR68 SPPL3 TMEM59 NCL PJA2 SDHA SLMAP CBX3 TMEM161B-AS1 PPOX ACBD3 GOLGA7 GTF2H1 MAP3K2 COA6 PHF5A COPS7A NAA10 SNX14 CUL1 ALG13 TNIK STARD3 LINC00892 SNAI3 RABAC1 USP42 NDUFA3 DDX58 PSMC6 RBCK1 HNRNPH3 ZFAND6 SDHD CHCHD10 PUM1 LDLRAD4 TCHP CNOT11 KIF1C SEC61G TACC1 PSMD6 NIPBL DAPP1 RAB2A TRIM5 LMNB2 SUPT6H AP3S1 CLK1 NSUN2 NUP88 PPA1 UMAD1 SETD9 DCAF15 ANXA2R TMEM9B EGLN2 MRPS18B PSD4 GTDC1 SUCO BUD31 IER2 UPF3A PINX1_ENSG00000254093 NUDC TAF7 UTP15 PTPMT1 PSME2 MALT1 PSTPIP1 TSPYL1 CTSD HIGD2A SBF1 LINC00667 FEM1A RAB5C TAF2 SHOC2 RASL11A AMZ2 TRIM65 ATP6V0E1 HEBP1 PSMD1 NDUFAF8 CORO1B MRPL34 UBN1 RAE1 SPINT2 SOCS1 TRIM52-AS1 TFDP1 TES YIPF2 STX5 BZW1 PDCL3 NOLC1 TPM3 INTS11 RAB35 COQ10B ZNF480 C1D THAP4 EBAG9 FOXP1 SLC30A6 NUS1 MRPS24 PPP2CA USP4 C9ORF16 MSH2 UQCRC1 SLC38A1 ACO2 WASHC5 TXLNG PHF20L1 RAB6A SERINC1 YTHDC1 MIF4GD C1GALT1 KRR1 NDFIP1 GMFG DCAF8 SNX6 PAIP2 PLAA SRP19 RAD21 TECR OTUB1 CLK3 BNIP2 NEK9 COPE RAB9A RMC1 JOSD2 MAP3K1 RNF144B ADGRE5 UBA5 NLRC3 LYPLAL1 SURF6 PDE9A ZNF720 C17ORF107 SPPL2A OPTN LACTB DNAJA2 CAMK4 PLP2 ARMC6 IDH2 FUNDC2 SNHG3 CAPG TMEM106C DR1 PGGHG TUT7 CYB561D1 EIF3E FURIN VPS26B APBB1 NTAN1 ATP5MF MAX MRPL14 ATP5ME ORAI1 SEC62 TNFRSF1A HTT EIF2AK4 SCAPER POLB FAM204A RRP12 ABHD5 IMPDH1 TAF1D TDG IMP4 SMC6 TRIM28 FAS ILK PPP1R16B TSR3 NOP56 EIF5B CUTA GLO1 PPP3CB CENPK MTRR ITGAE GOLGA3 GFER EPSTI1 THEMIS TRIAP1 HSD17B7 TSTD3 ANKRD39 PIP4K2A MTFP1 FAM222B SRFBP1 HOXB2 TRAPPC5 SRSF8 HECTD1 CCAR1 XKR8 RBM26 ISCA1 TRBC2 UBE2B TIMM8B RPUSD1 SAT2 FAM102A ST6GAL1 METTL9 C2ORF68 PERP SNU13 ANKZF1 CBX5 GPN1 COMMD8 EGLN1 HERPUD1 SENP6 SLC38A2 AARS1 GNL2 G3BP1 GPR108 PSMA4 SP100 MFSD10 TMEM70 QRSL1 DAD1 RPS19BP1 SNHG1 CSDE1 TAF4B ASF1A N4BP2L2 RNF138 FBXL5 JAK1 NCLN RAB11B PPP1CA ITGB2 KDELR1 PSMB1 MTX2 SLC35E2A NDUFS8 HAGH PIGW IVNS1ABP SDHC POLDIP2 EIF4G1 ANKRD36 SEPTIN7 CSNK1D PSMF1 TM9SF4 GAB3 MYL6B HNRNPL WIPF1 GATAD1 RFFL PRKCQ-AS1 GTPBP10 INO80D IFITM3 GAS5 WASHC1 CTNNAL1 PTGER2 USP33 ARRB2 ARMCX6 GMIP UMPS TRIM22 DDX41 SFPQ RBM25 MRPL38 CNP FAM193B MAP3K11 WDR74 PIGC CDC37 PPIP5K2 TRMO AGTRAP JMJD8 ARID5A ALKBH7 CYTH1 SET PAF1 SNHG5 FAM174B ADI1 SUDS3 CDCA7 GTSF1 MTCH1 TGIF2 MLX PYGO2 EIF2S1 DNPEP IL23R RPA1 CDC37L1 GYG1 ANP32A SRSF2 RNASEH2B WDR44 ZPR1 GIPC1 RRM2B ILVBL ARL2BP BRK1 YIF1A CEP164 DPY30 NUDT7 PPP2R1A R3HDM1 MSN PSIP1 HMGN4 CCDC152 STOML2 EEF1AKMT2 H2AZ2 CHST12 CHFR FKBP15 SPNS1 APH1A NR4A3 CNOT9 GPSM3 TESK1 CDK5RAP1 TNFAIP1 FAM118B ZBTB7A PCED1B-AS1 MRPS18A BUB3 ICMT DDIT3 DNM1L DCTN4 PPIG CCDC91 LZIC CHD2 BIRC2 AHSA1 ARAP2 COMMD10 NUCKS1 ASAP1 DCTPP1 ETFDH NAA80 STAU2 CCNL1 CASP3 CCDC174 PRNP APOA1-AS PPP1R11 SRP68 C1ORF43 RASSF1 MYD88 AKAP13 SLC25A5 ERH UBE2I ATP5PD ORC3 NUP37 TTC31 WBP2 PDE7A KPNB1 ETFA STK25 PJVK COX19 ATP5MC3 PPP2R2A MDH1 PSMB6 GTF2E2 B3GNT2 TSC22D4 GPD2 MBIP ZFR JTB GPRIN3 LPIN1 SLC35A1 NXT1 KLHL22 DYNC1I2 AP1S1 PPP1R15B SMIM15 TMEM65 HAUS3 KLHL28 MRPL20 MVK ATR MIB2 YPEL3 SCOC WASHC2C SPRY1 LCLAT1 CCDC25 SLC43A3 CIAO3 WBP11 TRIM4 C17ORF49 DTX1 VAMP5 ZSCAN16-AS1 TNFSF13B ARHGAP15 CISH DCP2 GRPEL2 PHB AASDHPPT PUM2 ISG20 PFKM SLC2A1 NBL1 TMED4 COPS3 SRSF11 ZNF710 POMP TTC14 ZNF414 PISD TMEM219 MIR23AHG COQ4 OXA1L DNAJA3 KMT2A MAGOH LYPLA2 WSB1 POLR2B AKIRIN2 PSMB2 UIMC1 DDX18 TCOF1 TNFRSF14 STARD3NL CELF2 NABP1 PTP4A2 LRRC59 TMEM167B CAMK2G SLC4A1AP EIPR1 NUCB2 MED6 VEZT SP4 ARL3 USP12 ARHGAP9 VMAC FIP1L1 RP11-316M20 ADH5 TMC6 IRF1-AS1 LIG4 SNX1 MACF1 GNL1 TPM4 RMND5A LPAR6 DNAJB2 NOP53 CDK2AP1 FIS1 PITPNM1 SF3B4 SNAP47 S100PBP C4ORF33 CHST11 RGS18 C7ORF31 PSMD4 EFCAB7 TTC39C ANXA5 KRI1 FAM214B DUSP12 NR2C1 CBLB UNG LSM8 MYO9B GLMN RALA GTF3C6 ISCA2 DNTTIP2 SELENOK MRPS36 KPNA3 THRA PTS DUSP6 VTA1 PPARG SEPTIN9 UBE2N FRY BCAS2 CAMKMT SAMHD1 GBP2 TMEM50A STAT5A IL17A PCYT2 ISG15 MAPK3 PDCD4-AS1 TIAL1 RTF1 CSNK2B RBM17 LPCAT4 ST13 CNBD2 TMEM184B RNPC3 ARCN1 SMAP2 BLOC1S1 NAMPT ZNF597 EMG1 SLC66A3 HDLBP C11ORF58 SH3BP1 SMARCB1 RP11-726G1 ZNF564 IFI27L2 ATG3 C6ORF226 REL GIMAP6 OLFM2 OGFR BAZ1A RAB7A EIF3L TBC1D20 SDHAF2 PARP10 GAK FYN TBXAS1 PRDM1 FAM241A TMEM263 SRPK1 CD2BP2 SERGEF SURF4 MTERF4 PPP1R10 IRF9 USP14 ZFAND2B AK3 CYTH3 PEBP1 PLIN2 NARF ICAM2 ZRSR2 HELZ C14ORF119 DCAF5 RBM42 ITFG2-AS1_ENSG00000258325 ZNF524 CRNKL1 FASTK UBXN1 MYO1G QTRT1 FLOT1 DNASE2 STK40 USP47 RFLNB ZFYVE28 FHL1 TATDN2 SLC25A25 ATP6V1B2 GSAP MRPL10 PSMD11 ZNF821 RANBP9 TIPRL TMED10 EIF3M HMGN2 UHMK1 TRBV30 EMP3 HPS6 TTC17 OSBPL8 COMT ERP29 GRPEL1 ZNF595 ROGDI RAD23A LYSMD2 ANKRD44 FRMD8 SRSF10 BCL2A1 TBC1D31 ALDH9A1 CHMP1A GABARAPL2 RPS20 MRPS2 CAST TMEM200A H4C2 ATRAID CRYBG1 CRY2 OSBPL7 TRAPPC1 POLR2K TESMIN UBE2D1 GNAI3 SLC39A6 HMGCS1 TMEM138 TMEM223 CHP1 ATP5MJ MSRA SQLE ATRX ZNF207 PPP1R15A DNAJC3 SNX3 FBXO9 FAM162A BTF3L4 NKIRAS1 PRDX6 EHMT1 SYS1 USP36 STX4 TMED7 NR4A1 GNA13 LINC00299 ST3GAL5 IDH3A NAA16 SLC38A10 CRYBB2 B4GALT1 METTL26 MRPL54 TRAF3IP2 DTX3L NDUFA6 NDUFAF4 SUCLA2 CALHM2 GTF2IRD2B RP11-706O15 IK MAGI3 TCTA PBDC1 BRD7 IRF1 P2RY10 CTBS CUX1 PRKD2 PGK1 BCLAF1 IRAG2 CIAPIN1 TNFRSF25 SNRNP200 IL2RG PSMA6 UQCRC2 RTF2 SLC35C2 GNG2 ZBTB22 MEF2A NOP10 LY9 MAP2K2 COMMD1 TMEM203 VPS29 SOD2 CITED2 FLI1 FMNL1 MRTO4 RETREG2 FGFR1OP2 RNF41 UTP4 TKT DHX36 ID2 TMEM245 ERP44 ESF1 BAG3 NDEL1 DAP3 HM13 PTGR2 ZFAND3 UCP2 BIN2 MCF2L2 PHKB SUB1 PDE3B CEP250-AS1 RNH1 WDR1 FAM117A CAMTA2 UGCG LINC00294 PHF14 PTPN22 KATNBL1 NT5DC1 TRIM38 ZNF780A C19ORF53 SNRPG TDP2 EIF4H TMEM140 ZCCHC17 ANKLE2 CDS2 EPHB6 CCDC90B ATAD3C COL18A1 FBP1 AHCY AFTPH ZNF432 HSPA9 CMSS1 FGD3 ANXA7 SLC25A13 RNF5 KRT10 KDM2B-DT ATP5F1D SCAMP3 KIF2A SHMT1 MRPL57 CD5 MLH1 ACOX3 ZNF593 TTI2 TRAPPC10 SRP72 CXCR6 COPS5 TET2 HEBP2 OGT ANKRD13D CEBPZ MRPL23 UBE2V1 AP2M1 SLC39A4 LLGL1 DLD ACAA2 SCAMP4 PARP8 CXXC1 MAD2L2 GIT2 OSGIN2 FAM219A ERLEC1 PTPN7 TMEM41B SERTAD3 TCF7 ARPC5 NCBP2AS2 ZNF211 SNRPB2 B9D2 ANKRD27 TIPARP RAB1B SRM PARP16 EIF4EBP2 GDI1 ARPC5L CEACAM21 IVD EXOSC4 KDM6A STARD7 JAK3 CBR1 ARL6IP5 STK26 UFM1 ARHGAP18 ESD ISOC2 HCFC1 RING1 PRELID1 MGME1 NDUFC2 CELF1 PNP IFT27 KBTBD2 TUFM TSTD1 CISD3 C17ORF67 BUD23 SCRN2 CHCHD7 FARSA RP11-29H23-1 LINC02481 TPP1 THYN1 ARHGEF1 GCLM PRPF39 TRAPPC6B SLF1 SMIM3 EVI2A AZIN2 ASH1L API5 COG2 SLC25A22 CHD4 WRAP73 CDC26 UBE2L3 EPC1 ATP2B1 CCAR2 RRN3 UBA1 CSNK1A1 CASP8 PELI1 CNOT2 SRP54 VAPA MRPS33 NDUFB11 NAF1 UBE2G2 DVL3 TLE5 CALCOCO2 UQCR10 SIRPG COX7B NDRG1 EIF2S3 H3C4 SMCHD1 RAN MAPKAPK5-AS1 TSG101 HAX1 ANKDD1A DCTN3 CD55 PSKH1 PET100 IMMT SRGAP2C H3-3A PIGH RBM48 FAM177A1 TEFM FBXL4 MID1IP1 PRR5L SLC25A11 NGDN NCAPD3 SLC9A3R1 CDIP1 GRK6 SDF4 DENND2D CASP7 EFR3A NFAT5 PRELID3B TEX30 CIRBP CSNK1G1 MCRS1 CHMP2B TMOD3 PRKX POLR2C NSFL1C POLR1H TAP2 PIANP ARL6IP6 RIPK1 TDP1 SCAF11 SNRPN ZNF335 USP3 UBQLN2 ARMCX3 HSDL1 RSU1 TXNL1 TMEM101 BAD KIF5B AP1AR FAM104A GBP4 AC058791 NDUFB1 DPP7 NDUFB2 RANGAP1 SETX MAF1 NME4 TRGV10 TACC3 AP5Z1 PITPNB EDEM1 CTD-2095E4 ANKRD28 MINDY2 YPEL4 IGHV4-34 TRIM44 ARL6IP4 GART RAD18 SF3B5 BZW2 ECD ZYX HECW2-AS1 CNTRL RECQL PARP9 UPP1 PYCR2 ARHGAP30 TBC1D22B RASGRP1 STIM1 ZNF263 ZNF230 LDLRAP1 PDCD2 FOXO1 UNC119B RNF114 YPEL5 IFNGR2 PRRC1 EIF4B PDXK RAB8B RICTOR ARL8A ESCO1 RBM38 PPIF GMPS HDDC2 FBXO42 PTP4A3 SERPINH1 TRIM69 WTAP PEMT VPS72 WAS PPP4R3A CDV3 CETN2 VAC14 TRIM21 DAZAP2 ARF5',\n",
       " 'cell_type': 'CD4-positive helper T cell',\n",
       " 'tissue': 'ileum',\n",
       " 'batch_condition': 'A29',\n",
       " 'organism': 'Homo sapiens',\n",
       " 'sex': 'female'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_idx = 0\n",
    "arrow_ds[sample_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping cells into multi-cell samples\n",
    "\n",
    "Here we will construct an example multi-cell dataset by grouping cells together based on batch sample and tissue type. This will provide us example data for predicting the tissue type of a group of cells based on their gene expression.\n",
    "\n",
    "Since we want to maintain a train/test split and not leak cells, only train set cells will be used to construct the training multi-cell dataset. The same will be true for the test set, only test set cells will be used to construct the test multi-cell dataset.\n",
    "\n",
    "At a high level, the procedure will be:\n",
    "1. Build a dictionary mapping a tuple of (batch sample, tissue type) to a list of cell indices which belong to that batch sample and tissue type.\n",
    "2. For each sample in the original dataset, we sample 4 more cells from the dictionary which belong to the same batch sample and tissue type.\n",
    "    - This keeps the overall size of our dataset the same, but results in each sample having 5 cells. Note that samples can have variable numbers of cells and metadata, but for this tutorial we will assume that each sample has the same number of cells, genes per cell sentence, and cell metadata.\n",
    "3. Store the indices of multi-cell groupings in a new arrow dataset, so that the prompt formatter can build multi-cell sentences.\n",
    "    - The multi-cell indices dataset will have the same number of samples as the original dataset, and each sample will consist of a list of 5 cell indices, indicating which cells should be grouped together to form a multi-cell sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['cell_name', 'cell_sentence', 'cell_type', 'tissue', 'batch_condition', 'organism', 'sex'],\n",
       "        num_rows: 23847\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['cell_name', 'cell_sentence', 'cell_type', 'tissue', 'batch_condition', 'organism', 'sex'],\n",
       "        num_rows: 2948\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['cell_name', 'cell_sentence', 'cell_type', 'tissue', 'batch_condition', 'organism', 'sex'],\n",
       "        num_rows: 2978\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, add a column for train/test split to the original dataset\n",
    "split_ds_dict, data_split_indices_dict = train_test_split_arrow_ds(arrow_ds)\n",
    "split_ds_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll define a function that takes as input a Huggingface cell sentence arrow dataset and returns a dictionary mapping a tuple of (batch sample, tissue type) to a list of cell indices which belong to that batch sample and tissue type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_multi_cell_groupings(hf_ds):\n",
    "    batch_tissue_to_cell_indices_dict = {}\n",
    "    for sample_idx in range(hf_ds.num_rows):\n",
    "        # Load sample, get batch sample and tissue type\n",
    "        sample = hf_ds[sample_idx]\n",
    "        batch_sample = sample[\"batch_condition\"]\n",
    "        tissue_type = sample[\"tissue\"]\n",
    "\n",
    "        # If new batch sample and tissue type combination is found, add to dictionary\n",
    "        if (batch_sample, tissue_type) not in batch_tissue_to_cell_indices_dict:\n",
    "            batch_tissue_to_cell_indices_dict[(batch_sample, tissue_type)] = []\n",
    "\n",
    "        # Add sample index to dictionary\n",
    "        batch_tissue_to_cell_indices_dict[(batch_sample, tissue_type)].append(sample_idx)\n",
    "    return batch_tissue_to_cell_indices_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_batch_tissue_to_cell_indices_dict = get_multi_cell_groupings(split_ds_dict[\"train\"])\n",
    "val_batch_tissue_to_cell_indices_dict = get_multi_cell_groupings(split_ds_dict[\"validation\"])\n",
    "test_batch_tissue_to_cell_indices_dict = get_multi_cell_groupings(split_ds_dict[\"test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(batch sample, tissue type): [list of cell indices with this combination]\n",
      "('A29', 'ileum'): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "('A29', 'lung'): [281, 282, 283, 284, 285, 286, 287, 288, 289, 290]\n",
      "('A29', 'thoracic lymph node'): [2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815]\n",
      "('A29', 'mesenteric lymph node'): [6479, 6480, 6481, 6482, 6483, 6484, 6485, 6486, 6487, 6488]\n",
      "('A29', 'bone marrow'): [8762, 8763, 8764, 8765, 8766, 8767, 8768, 8769, 8770, 8771]\n",
      "('A29', 'skeletal muscle tissue'): [9895, 9896, 9897, 9898, 9899, 9900, 9901, 9902, 9903, 9904]\n",
      "('A29', 'liver'): [9909, 9910, 9911, 9912, 9913, 9914, 9915, 9916, 9917, 9918]\n",
      "('A29', 'spleen'): [10442, 10443, 10444, 10445, 10446, 10447, 10448, 10449, 10450, 10451]\n",
      "('A31', 'spleen'): [13351, 13352, 13353, 13354, 13355, 13356, 13357, 13358, 13359, 13360]\n",
      "('A31', 'ileum'): [15047, 15048, 15049, 15050, 15051, 15052, 15053, 15054, 15055, 15056]\n",
      "('A31', 'omentum'): [15073, 15074, 15075, 15076, 15077, 15078, 15079, 15080, 15081, 15082]\n",
      "('A31', 'lung'): [15099, 15100, 15101, 15102, 15103, 15104, 15105, 15106, 15107, 15108]\n",
      "('A31', 'liver'): [16114, 16115, 16116, 16117, 16118, 16119, 16120, 16121, 16122, 16123]\n",
      "('A31', 'thoracic lymph node'): [17792, 17793, 17794, 17795, 17796, 17797, 17798, 17799, 17800, 17801]\n",
      "('A31', 'caecum'): [20199, 20200, 20201, 20202, 20203, 20204, 20205, 20206, 20207, 20208]\n",
      "('A31', 'bone marrow'): [20257, 20258, 20259, 20260, 20261, 20262, 20263, 20264, 20265, 20266]\n",
      "('A31', 'thymus'): [20544, 20545, 20546, 20547, 20548, 20549, 20550, 20551, 20552, 20553]\n",
      "('A31', 'mesenteric lymph node'): [20826, 20827, 20828, 20829, 20830, 20831, 20832, 20833, 20834, 20835]\n",
      "('A31', 'skeletal muscle tissue'): [22719]\n",
      "('A31', 'duodenum'): [22720, 22721, 22722, 22723, 22724, 22725, 22726, 22727, 22728, 22729]\n",
      "('A29', 'caecum'): [23309, 23310, 23311, 23312, 23313, 23314, 23315, 23316, 23317, 23318]\n",
      "('A29', 'transverse colon'): [23525, 23526, 23527, 23528, 23529, 23530, 23531, 23532, 23533, 23534]\n",
      "('A29', 'sigmoid colon'): [23743, 23744, 23745, 23746, 23747, 23748, 23749, 23750, 23751, 23752]\n"
     ]
    }
   ],
   "source": [
    "print(\"(batch sample, tissue type): [list of cell indices with this combination]\")\n",
    "for key in train_batch_tissue_to_cell_indices_dict.keys():\n",
    "    print(f\"{key}:\", train_batch_tissue_to_cell_indices_dict[key][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(batch sample, tissue type): [list of cell indices with this combination]\n",
      "('A29', 'ileum'): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "('A29', 'lung'): [38, 39, 40, 41, 42, 43, 44, 45, 46, 47]\n",
      "('A29', 'thoracic lymph node'): [371, 372, 373, 374, 375, 376, 377, 378, 379, 380]\n",
      "('A29', 'mesenteric lymph node'): [817, 818, 819, 820, 821, 822, 823, 824, 825, 826]\n",
      "('A29', 'bone marrow'): [1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106]\n",
      "('A29', 'skeletal muscle tissue'): [1232, 1233]\n",
      "('A29', 'liver'): [1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243]\n",
      "('A29', 'spleen'): [1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299]\n",
      "('A31', 'spleen'): [1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675]\n",
      "('A31', 'ileum'): [1905, 1906]\n",
      "('A31', 'lung'): [1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916]\n",
      "('A31', 'liver'): [2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026]\n",
      "('A31', 'thoracic lymph node'): [2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217]\n",
      "('A31', 'caecum'): [2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502]\n",
      "('A31', 'bone marrow'): [2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512]\n",
      "('A31', 'thymus'): [2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545]\n",
      "('A31', 'mesenteric lymph node'): [2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578]\n",
      "('A31', 'skeletal muscle tissue'): [2810]\n",
      "('A31', 'duodenum'): [2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820]\n",
      "('A29', 'caecum'): [2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895]\n",
      "('A29', 'transverse colon'): [2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919]\n",
      "('A29', 'sigmoid colon'): [2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938]\n"
     ]
    }
   ],
   "source": [
    "print(\"(batch sample, tissue type): [list of cell indices with this combination]\")\n",
    "for key in val_batch_tissue_to_cell_indices_dict.keys():\n",
    "    print(f\"{key}:\", val_batch_tissue_to_cell_indices_dict[key][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(batch sample, tissue type): [list of cell indices with this combination]\n",
      "('A29', 'ileum'): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "('A29', 'lung'): [27, 28, 29, 30, 31, 32, 33, 34, 35, 36]\n",
      "('A29', 'thoracic lymph node'): [370, 371, 372, 373, 374, 375, 376, 377, 378, 379]\n",
      "('A29', 'mesenteric lymph node'): [821, 822, 823, 824, 825, 826, 827, 828, 829, 830]\n",
      "('A29', 'bone marrow'): [1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105]\n",
      "('A29', 'liver'): [1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247]\n",
      "('A29', 'spleen'): [1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318]\n",
      "('A31', 'spleen'): [1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656]\n",
      "('A31', 'ileum'): [1883, 1884, 1885, 1886, 1887]\n",
      "('A31', 'omentum'): [1888, 1889, 1890, 1891]\n",
      "('A31', 'lung'): [1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901]\n",
      "('A31', 'liver'): [2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033]\n",
      "('A31', 'thoracic lymph node'): [2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220]\n",
      "('A31', 'caecum'): [2520, 2521, 2522, 2523, 2524]\n",
      "('A31', 'bone marrow'): [2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534]\n",
      "('A31', 'thymus'): [2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577]\n",
      "('A31', 'mesenteric lymph node'): [2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616]\n",
      "('A31', 'duodenum'): [2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853]\n",
      "('A29', 'caecum'): [2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924]\n",
      "('A29', 'transverse colon'): [2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952]\n",
      "('A29', 'sigmoid colon'): [2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977]\n"
     ]
    }
   ],
   "source": [
    "print(\"(batch sample, tissue type): [list of cell indices with this combination]\")\n",
    "for key in test_batch_tissue_to_cell_indices_dict.keys():\n",
    "    print(f\"{key}:\", test_batch_tissue_to_cell_indices_dict[key][:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define another function which will take as input a Huggingface cell sentence arrow dataset and a dictionary mapping a tuple of (batch sample, tissue type) to a list of cell indices, and return a new arrow dataset containing the multi-cell groupings.\n",
    "\n",
    "Note that if we need metadata for the multi-cell samples, we should add a new column to the multi-cell arrow dataset in this step that contains the metadata for each multi-cell sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_multi_cell_arrow_ds(hf_ds, batch_tissue_to_cell_indices_dict):\n",
    "    \"\"\"\n",
    "    Function to take in a Huggingface cell sentence arrow dataset and a dictionary \n",
    "    mapping a tuple of (batch sample, tissue type) to a list of cell indices, and \n",
    "    return a new arrow dataset containing the multi-cell groupings.\n",
    "\n",
    "    Arguments:\n",
    "        hf_ds: Huggingface arrow dataset containing cell sentences to group into multi-cells.\n",
    "        batch_tissue_to_cell_indices_dict: Dictionary mapping a tuple of (batch sample, tissue type) to a list of cell indices.\n",
    "    \n",
    "    Returns:\n",
    "        multi_cell_groupings_ds: Huggingface arrow dataset containing multi-cell groupings.\n",
    "    \"\"\"\n",
    "    # Initialize list to store multi-cell groupings, each element is a list of 5 cell indices\n",
    "    multi_cell_groupings_list = []\n",
    "    batch_sample_labels = []\n",
    "    tissue_type_labels = []\n",
    "    organism_labels = []\n",
    "\n",
    "    # Loop through each sample in original dataset - keeps number of overall samples (roughly) the same\n",
    "    for sample_idx in tqdm(range(hf_ds.num_rows)):\n",
    "        sample = hf_ds[sample_idx]\n",
    "        batch_sample = sample[\"batch_condition\"]\n",
    "        tissue_type = sample[\"tissue\"]\n",
    "\n",
    "        # Retrieve list of cell indices for this batch sample and tissue type\n",
    "        sample_key = (batch_sample, tissue_type)\n",
    "        all_cell_indices = batch_tissue_to_cell_indices_dict[sample_key]\n",
    "        if len(all_cell_indices) <= 5:\n",
    "            # If there are less than 5 cells for this batch sample and tissue type, we will sample with replacement.\n",
    "            #  Note that another option here would be to just skip this sample.\n",
    "            sampled_cell_indices = random.choices(all_cell_indices, k=5)\n",
    "        else:\n",
    "            # If there are 5 or more cells for this batch sample and tissue type, we will sample without replacement\n",
    "            sampled_cell_indices = random.sample(all_cell_indices, k=5)\n",
    "\n",
    "        # Add list of sampled cell indices to multi-cell groupings list\n",
    "        multi_cell_groupings_list.append(sampled_cell_indices)\n",
    "        batch_sample_labels.append(sample[\"batch_condition\"])\n",
    "        tissue_type_labels.append(sample[\"tissue\"])\n",
    "        organism_labels.append(sample[\"organism\"])  # all cells should be of the same organism\n",
    "\n",
    "    multi_cell_groupings_ds = Dataset.from_dict({\n",
    "        \"multi_cell_groupings\": multi_cell_groupings_list,\n",
    "        \"batch_label\": batch_sample_labels,\n",
    "        \"tissue\": tissue_type_labels,\n",
    "        \"organism\": organism_labels,\n",
    "    })\n",
    "    return multi_cell_groupings_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 23847/23847 [00:02<00:00, 11379.28it/s]\n",
      "100%|██████████| 2948/2948 [00:00<00:00, 11399.13it/s]\n",
      "100%|██████████| 2978/2978 [00:00<00:00, 11434.93it/s]\n"
     ]
    }
   ],
   "source": [
    "train_multi_cell_arrow_ds = get_multi_cell_arrow_ds(split_ds_dict[\"train\"], train_batch_tissue_to_cell_indices_dict)\n",
    "val_multi_cell_arrow_ds = get_multi_cell_arrow_ds(split_ds_dict[\"validation\"], val_batch_tissue_to_cell_indices_dict)\n",
    "test_multi_cell_arrow_ds = get_multi_cell_arrow_ds(split_ds_dict[\"test\"], test_batch_tissue_to_cell_indices_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look and double check that all cells in one sample belong to the same batch sample and tissue type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cell_name': 'Pan_T7935490_AAACGGGCATCTGGTA',\n",
       " 'cell_sentence': 'TMSB4X MT-CO1 B2M MT-CO2 MALAT1 HSPA1A ACTB HSP90AA1 RPLP1 MT-CO3 MT-CYB RPS27 JUNB MT-ND3 EEF1A1 HSPA1B HLA-B RPL41 RPS18 CD69 DNAJB1 JUN CCL5 RPL10 RPL28 FOS MT-ND4L MT-ATP8 RPS19 IL32 TPT1 RPL39 IFNG RPL13 FTH1 PPP1R15A RPS15A RPS14 PFN1 ZFP36 HLA-C CD52 EIF1 SH3BGRL3 PTMA MT-ATP6 RPS12 FTL ACTG1 NR4A2 RPS3 RPS15 GAPDH RPLP2 RPL11 HLA-A RPS4X TNFAIP3 RPL34 RPL18 RPL32 RPL37 RPL30 MT-ND1 RPL8 RPL19 RPS23 RPL18A MT-ND2 GZMA UBC NKG7 RPL21 RPS21 RPS27A RPS28 RPL35A GADD45B H3-3B RPS24 RPL3 RPL29 RPS6 VIM SELENOK RPLP0 S100A4 RPL26 RPS13 CD8A MTRNR2L12 RPS25 CD3E CD3D RPS7 FAU CFL1 RPL12 DUSP1 RPS8 HSPA8 RPS3A RPL9 SRSF7 RPL14 HLA-E RPL36 TRBV9 MT-ND5 RPS2 RPL27 CRIP1 ARHGDIB TMSB10 RAC2 MIF MT-ND4 H4C3 DUSP5 IER2 ZFP36L1 RPS9 MYL12A MYL6 NFKBIA RPL7A TAGLN2 RPS16 UBA52 PPIB RPS5 SERF2 RPL23A SRGN RPSA RPL24 RPL22 ATF3 PCBP2 RPL15 S100A6 PNRC1 DDIT3 RPL4 CALM1 NR4A1 TRAV4 EEF2 HOPX TPM3 ATP5F1E CORO1A CALR COTL1 UBB GZMM EVL CCL4L2 RPS29 IL2 RGS1 RPL38 CD7 SARAF HCLS1 CYBA PAXX PDCL3 ANXA1 LGALS1 ID2 CD3G PTGER4 HERPUD1 PRMT9 BTG1 HSP90B1 OSTF1 HNRNPA2B1 HSPE1 RPL37A HSP90AB1 CITED2 DDX5 RPL35 RGS2 PPIA IFITM1 PPDPF LSP1 OAZ1 RPS11 H1-10 RSRC2 ARPC1B NPM1 TMA7 S100A11 NACA RPL31 DUSP2 RPS26 RPL17 IFITM2 ALDOA CD74 SSR2 CD96 RPL10A STK17A ARL6IP4 GEM SLC2A3 DDIT4 HCST PTPRCAP CAP1 ITM2B GPX4 VAMP2 SNHG15 PSMB10 RAN PFDN5 UQCRB SAMSN1 ARPC3 B3GNT2 TAF7 PPT1 SAT1 SRP14 TSC22D3 MYO1G ATP5MK ISG20 ARPC2 H2AX CDC42SE2 HSPH1 ATF7IP FKBP1A SURF4 EEF1D ZFP36L2 FYB1 CD70 NSD3 SKAP1 RPL36A MYL12B SCAF11 TRAPPC1 TRAC CNN2 ZBTB38 MAP3K8 REL DAZAP2 EMP3 CLEC2D SNRPD2 GUK1 PSME1 NDUFB4 GLO1 MT-ND6 LDHA PDIA6 RPL36AL IER5 BNIP3L COX4I1 FERMT3 GNG2 CYCS EEF1B2 MRPS34 CLDND1 YPEL5 ALKBH5 HSPA5 TAGAP ABRACL TLE5 PTPRC NDUFAB1 TMEM170A HNRNPA3 BCAS2 RAP1B WIPF1 HNRNPDL KLRK1 RPL13A CNOT2 CD37 CALHM2 CDK2AP2 UQCR11 SEPTIN1 IRF1 H3-3A AKAP13 GIMAP6 ATP5MC3 NME2 HINT1 ATP5F1D ZFAND2A HNRNPA1 TPI1 PDE4D CALM3 RACK1 TBC1D10C FOSB GOSR1 IQGAP1 NCL COX6C STK17B NDUFB2 FLOT1 MATK XCL1 PLAAT4 NOP53 SUMO2 SPOCK2 TRAV12-1 HLA-DRB1 DDX24 PABPC1 TRIM22 TRABD RAD54L2 SLC25A6 GIMAP1 SAP18 GBP2 RPL5 BCAP31 PRDX5 ENO1 APRT TAP1 EIF4A2 UBL5 KLF6 RBMX TAF15 SEC62 EIF5A MGLL COX7A2 LCK DNAJA1 RNGTT TAPBPL PRKD2 MPHOSPH10 IL2RG RBM25 TSEN54 PSMB9 ACTR3 COPS9 NDUFB1 HMOX2 RASSF5 RAPGEF1 POLR3E COX8A NUP214 RSU1 ELF1 RPS20 NOP10 TMEM18 HNRNPF NEMF AIP VCP TMED9 LIME1 PRXL2B PRKCH COX6B1 CCDC69 UXT CD2 ERGIC3 UBXN1 SNRPA1 CDC37 ARF4 TMEM109 LMO4 TOMM7 TRBC2 BCAS4 BCL3 EIF3F RHOG PPP1CA MAP1LC3B MAP7D1 EIF3D PTGER2 COMMD6 SLC9A3R1 ITM2C TLN1 NFAT5 EFHD2 UBE2D2 ZBTB25 SNX3 PDK3 PPP2R1A ARHGEF3 ZC3H12A SNHG9 NDUFC2 CDC14A TNFSF9 CD44 COMMD5 RBM17 SH3GL1 TMIGD2 ABI3 CD6 UPF2 WDR1 CHMP4A EIF3K ATP6V1G1 JUND AGO3 MBNL1 NDUFA5 RAB11A MYADM MATN1-AS1 ITPA GOLGA4 CAPNS1 CLEC2B PIK3R1 C19ORF12 CYTIP NDUFV1 ATP2A3 RPIA ZNF706 PPID SCAND1 MALT1 LIMD2 STARD7 GABARAPL1 SMURF2 MFSD14A CHMP2A CSRNP1 TXN2 OTUB1 SELENOW RP9 AP2M1 HMGA1 RPL22L1 KLRD1 RAB5C EMC6 SPN UBE2A RSRP1 RNF7 VAPA LY6E SNRPF IK ADRM1 ZFAND5 CAPG PPIG CCL4 HNRNPL LCP1 SH3BGRL SELENOH CDR2 PDCD4 CTSD CHASERR TMEM263 CDC42EP3 SS18L2 SMARCAL1 KTN1 NDUFB9 PKN1 SNRNP70 PKM PHF20 NUB1 BRD2 ARL4C CPSF7 ARPC4 TCF25 HSD17B10 MED12 ATP5F1B KMT2E MGST3 SKP1 BBX ASXL2 AATF TUBB FDX1 ARRB2 NUFIP2 RNASEH2C GMEB1 DDX21 ITGB7 RELB TERF2IP CSNK2B CFDP1 S100A10 DPM2 PANK2 RPL27A HNRNPC OST4 VAMP8 METTL23 ZC3H7A SEC61B YWHAB SMIM7 PHYH ATP5MG FBXL12 TRAM1 PELO SRRM1 TNRC6C P4HB CISH CDK10 NEDD8 TRG-AS1 ABCF1 CD8B EIF3I CDK9 MTRF1L TCEA1 SMG6 NCF1 TXNL4A NDUFA13 COX7C ATP5PO ODC1 ARHGAP9 RNF167 SDCCAG8 SEC11C SSR4 ANXA2 BTBD10 RAB27A TPP1 FAM53C TTC39C CHD4 UBAC2 PLXNA3 SCML4 IKZF3 ROCK1 CLIC1 GRAMD1A CSDE1 VPS26C COX17 CCNI ARHGDIA RNASEK TSC22D4 RPL6 HAX1 HMGB1 TPD52 ZNF331 SELENOS SYNRG ANKHD1 ENSA SUCLG2 SEPTIN9 RPL23 AIMP1 CEP68 NABP2 MYH9 TMCO1 CNBP LINC01138 BNIP2 PPP4R2 PDE4B SNHG5 RBM5 SRSF4 SRSF3 PAG1 ZNF431 MCL1 COX7B PPP1R21 CIRBP ELOB CCND3 CTSC THUMPD3-AS1 COMMD8 RGS10 LSM4 RAD9A LINC01871 CLK1 RALGAPA1 NAPA ATP6AP2 GMDS SMIM26 YBX1 RAB35 BRD9 DNAJB6 HMGN1 CMPK1 ALG13 CD99 KDM6B MBNL2 KRCC1 FAM133B MLLT6 MSN STIP1 RNASET2 XRCC5 CBX3 SNRNP27 AUP1 CKLF DTX3L SNHG29 CCND2 EMD RESF1 ZNF862 UBE2M FBXL14 ARID4A ANKRD10 RP6-65G23 RENBP DENR MKRN1 ISG15 MRPS6 POLD2 GOLGA8A DGKD ZNF266 MLEC POLR2L LY9 GSPT1 MX1 LINC-PINT TRPM7 PCGF6 MRPL48 NUDT14 CCT6A KIAA0232 FKBP4 DIAPH1 PLEKHB2 TRIM33 SCAMP2 CRBN PSMB7 UBE2Z EFCAB14 RANBP2 BRD4 CARS2 FAM104B FBXO34 KHK SPAG7 CACTIN SDF2 SRSF11 TMED5 IFRD1 AKIRIN2 EVI2A SFT2D2 TPM4 RPA2 ACAP1 SLC25A44 NAT10 NMUR1 CHD9 NFATC3 SRP54 KMT2C FAM126B MRPS35 HPRT1 GALK2 MZT2B DTWD1 TRMT6 SERPINB1 GTF2H5 WASHC1 HDAC1 KLRC1 WTAP MAP4K2 BLOC1S4 TMEM14B KDELR2 PPP4C AGAP2 UIMC1 ERLEC1 MRPL4 CD244 POLR1H SERINC1 TBC1D9B TUBA1A LANCL3 SLC38A1 GTPBP1 NASP PTPN7 DIS3L2 RPS6KA1 NSUN5 SSBP2 STRAP SHOC2 VPS51 IRAK1 DDX59 EIF5B ATP5PD RP11-498C9-2 MSH3 ADA RP3-370M22 GARRE1 MOB3A KMT2B WBP2 CNOT1 SPCS3 SSR1 NARS1 FBXO25 RP11-705C15-1 MYDGF MORF4L2-AS1 CAPZA1 FAM50A PTTG1 TYK2 MRPL37 AZIN1 SP3 ZNF518B GIT2 ABHD5 MAP4K1 H2AZ2 PRPF19 LTA4H RBM39 PSMA6 ZCCHC10 TMEM273 SPEN EIF4G2 SSB NEDD9 SCRIB TXNDC15 WDR27 PHF3 MRPL41 PTPN6 PIGL PRDX1 RNF13 UQCRC2 AREL1 C12ORF66 ERVK13-1 NFKBIB IL21R ETHE1 CELF2 PRKCZ DDX39A BIRC3 GIMAP4 FOXN3 AKR1B1 ZRANB2 TSPOAP1 MFSD2A HECA TNFRSF1B YWHAE PACS1 CSKMT MRPL20 SLC12A7 KPNA2 FUT6 SPPL2A ZCCHC17 WDR53 UBE2D3 UQCRC1 TRIM62 PSMD11 C11ORF49 TCP1 GABARAP TPD52L2 SUPT20H FNBP4 KDM6A RAB21 MRPL18 SMDT1 DNAJC15 UBE2J2 LTBP4 CCT2 SPAST FAM3C TBCD VPS13C SHKBP1 PSME3 ATG3 ARPP19 HNRNPH1 CELSR3 PHF5A JTB POP5 RNF10 ILF3 CUTC ATP5PF GPS2 PREX1 PLAA FOSL2 HNRNPA0 IRF1-AS1 PCTP CD55 TRA2A RABIF PIP4K2A ARHGAP45 MRPL22 SPECC1L ANXA5 SLC25A3 CEBPZOS MRPS25 LARP4 GPSM3 GTPBP6 ATP2B4 WDR45 SDCBP POLG PDHX PPM1M KLRB1 ERICH1 SLA2 YIPF3 TMEM106C LAPTM5 NIPSNAP2 EBPL MECR GMFG TNRC6B TTC1 MPLKIP GNAO1 CCT7 FAM13B MICA ZFYVE28 CRCP B3GALT6 C2ORF49 MAPRE1 TRPT1 NDUFA3 NORAD PSMC3 IRF2 ACAD11 BZW1 STX6 RPP21 GSTCD PPP1R13L CHD1 PYURF HDHD3 GPATCH11 ATP5ME HNRNPH3 DERPC CST7 PGRMC1 HPS5 BMT2 ACADS HLA-DQA2 TOMM40 SMIM14 GORASP2 MARCHF7 ATP5MJ ARL8B CSTB CHRAC1 ZNF644 CD9 YY1 TINF2 PARK7 CDC42 NFE2L1 INIP LMBR1 SIN3A IFT80 CD160 SPAG16 KXD1 RABEP2 POMC FCMR IFI27L2 TXN ENTPD1 PLCB3 CHCHD10 RP11-358L22 MDH2 HIGD2A GSC SPCS2 DNAJB11 LSM6 PURA CD200R1 LMAN2 PEX11B RCSD1 ELOC CCDC117 HECW2-AS1 BCKDK COPZ1 LPCAT3 MTF2 VRK2 TNFSF12 TMEM160 MFSD10 PSMB1 CHKB ERN1 ATP6V0E2 ELF3 MAD2L2 TFE3 ARAP1 SRP72 FUNDC2 RNF11 SLC9A8 AC058791 CFLAR GPR15 FAM102A NECAB3 PNRC2 SF3B6 PSMD14 RB1 MRPS24 RNF121 RELL1 DHRS4 CTDSP1 NDFIP1 MTRNR2L8 ARMC10 SASH3 SPIN4 SHMT2 EMB POLR3GL LPXN ABCA2 GORASP1 MPHOSPH8 C11ORF58 SUPT3H RP11-420L9 SERPINB6 ELMOD3 DHX40 MRPL34 CALU NSD1 OSBPL3 LMNTD2 TMED4 VPS37B FAM91A1 SAT2 SLC43A3 MEAF6 DCAF5 TRAPPC5 GSTK1 PARVG PDE3B SNRNP25 ANKRD28 DERL2 TMEM150B EPOR NIPSNAP3A DFFA NSMCE2 NHEJ1 GTF3A GPR35 PIGS PTP4A2 TOX4 TMEM242 PRDM1 JAK1 POP4 CD2BP2 LITAF FAM228B FAM162A BLM STRIP1 FEZ2 MED20 RNF111 STAT1 MRPL24 PRKAA1 TGFBR1 NECAP2 SNRPE SDHD SEPTIN11 UNC45A TOLLIP ADAM15 TENT5C GBP1 NFYC IDI1 STK4 XBP1 SSRP1 APTX DUT NPC2 DRAM2 LAMTOR1 DNAJA3 KDM2A CYB5R3 WDR33 MAPKAPK3 PSMB3 PBXIP1 BORCS7 DUSP6 ASB6 USP10 STARD3NL SFT2D1 SOD1 TMEM230 UHRF2 RAB5B HIGD1A PIM1 SPATA13_ENSG00000182957 BFAR BCL7C PITPNM2 BATF ARF6 DOCK8 AKIRIN1 ECHDC2 SCAP RBM10 PARG SLC9A1 SPG11 IVNS1ABP RTF1 SF3B4 ABI1 RBIS ANTKMT ATR POLR2E COG5 TIMM8B HCFC1R1 GOSR2 SERP1 SNHG16 TWF2 MACO1 PYROXD1 DESI2 DCP1A DNAJB2 DCXR ANP32E TMEM156 PDS5A UBA5 UBE3C FNDC3B USP36 PGAM1 DNM2 SRP9 CTNNA1 ITGAL KDM5A FAAP20 RPS10 KPNA4 MMADHC RAB5IF BSCL2 DDX46 GALC DPP9 ARF1 TRA2B GMPR2 RABAC1 RHOA SOCS3 RHOC MORF4L1 CAPZA2 RBMS1 TUBGCP4 RP11-475M20 MRPL33 SMARCD1 UFM1 WDR48 EZH2 HUWE1 ARID2 OPA3 FAM160A2 MPHOSPH9 RPS10-NUDT3 CCDC85B RNF139 SAMD9L ZNF335 APEH DYNLT1 SEC22B AP2B1 RPL7 RAB14 POLR2H COX5A MRPL38 LSM8 RSPRY1 EPC1 PSMB8-AS1 VOPP1 RANGRF RFK GGCT SERBP1 NAA50 MSANTD3 GALNT6 SH3KBP1 SF3A2 PSMA1 TUBA4A HLA-DMA PLEKHF2 GPR137 BRWD1 OGFOD2 TUFM SERTAD1 PFDN2 RNASEH2B CD53 FNBP1 SIGIRR ANKLE2 TSSC4 PTPN22 SEC23B CD151 TCIRG1 SMG7 PTGES3 VDAC1 THOP1 APH1A UBE2F JMJD6 NAGLU HELZ TMEM138 COX6A1 GIMAP7 PHAX CRY1 ZKSCAN1 PIN1 IMP3 LRP10 SECISBP2L CREB3L2 SNW1 MIS12 TBC1D23 AFF4 METTL9 TUBA1B OSBPL7 PDIA3 TBRG1 HLA-DQA1 CYB5R4 RHEB HSPBP1 POLR2G BCCIP CYRIB ST13 RP4-620F22 KRBOX4 GPRASP1 TMEM159 UBA7 MAFF CCDC91 BZW2 WASHC2C NDUFA11 P2RX7 WHRN C6ORF120 LEMD2 NAP1L1 GTF2H1 MTERF1 LAIR1 TUBB4B CAPN7 LEO1 RGS19 POLR2K GNPDA1 BTF3L4 BET1 TMEM191C R3HDM4 ARL8A SNRPC MMAA SRPK2 TMEM179B SMG9 NSMCE1 PRR7 MYO9A PITHD1 MAF1 CCL28 RACGAP1 RO60 RAP2B U2SURP SLC14A1 SLC30A9 USP42 GATAD2A TFAM RPS17 TMEM256 PPP3CA ATP5MC2 RASGRP1 MARS1 UGDH CRYBG1 ANKRD37 SZT2 MPV17 TRAF3IP1 TBK1 ABCB8 MCOLN2 NOL4L MTFP1 POLR1D TOMM6 C9ORF78 TMF1 TRIP10 ZFAS1 ANKRD9 TOP1 FAM168B GPATCH1 PHPT1 PET100 TMEM208 HLCS GNA13 UTP18 SFT2D3 CKAP2 NSMAF C4ORF3 FAM177A1 ALOX5AP PIEZO1 YWHAQ NPEPPS DDRGK1 DDX19B ERCC8 PPP6R1 DUSP10 RNF19A MTPAP LINC01128 GPR65 ARID4B OPA1 TRIP11 GMCL1 TOMM5 ORAI1 EIF3E DSP SPTLC2 BIN1 ARRDC1 ANKRD12 TPCN1 HGS CD247 ANAPC16 ZNF296 EOLA2 EID1 IGKV4-1 UBE3A TNF ODF2 LPAR6 APOBEC3G ARHGAP25 POLDIP3 MGAT4A B4GALT5 HARS1 WBP11 ZNF746 LNPEP CASC3 CTB-31O20-4 CAMK2G IBTK CIZ1 EXOC2 PNPLA2 COQ4 REST TAF12 KEAP1 ZNF7 DECR1 MRFAP1 SELENOM MEX3D HSPA9 SMCHD1 CMTM3 RER1 ARID5A SPRY1 LAMTOR4 ANKRD44 RMND1 EIF2S2 GMPPB RAB24 TBCC TECPR1 FAM204A ARL2BP PPIH MIR23AHG RCL1 UFD1 SRF ARL3 MOSPD3 GRK6 H1-3 AKAP1 MT2A C19ORF25 TCTA PUM2 CCDC88B RASSF1 UBE2Q2 CNP SPCS1 C2ORF74 CYSTM1 BRK1 FAM114A2 EIF2A KLRC2 RWDD3 NPLOC4 EIF4G3 TRIM41 APBB1IP CSRP1 COPG1 SLC25A39 LARP4B NDUFA2 KIF5C NELFCD SAE1 DGKA SERPINA1 MIF4GD NDUFB5 GAB3 ATN1 RAB1B SRSF2 ZNF544 TOR1AIP1 RIT1 NMRK1 GPRIN3 CCSAP DNLZ PPP1R12A PPP1R18 IDH2 SDF2L1 MRPL55 VPS28 PCBP1 ATP6V0E1 THRA ARFGEF1 CSNK1A1 ACP1 GZMH STX17 ABT1 MITD1 GAS5 TRIM14 STMN3 PLEKHM1 UBE2V1 PTGES2 CARD11 TNFAIP8 PRPF8 UPF1 TRAPPC2L TRIM52-AS1 DNAJB9 ACSL5 RIN3 OXSR1 REPS1 PCNX3 REX1BD LUC7L3 ATG12 PSPH GPR174 FARSA ZNF22 ARHGEF6 GTF2A2 CAPN15 NRDC SCP2 MIEN1 TMEM258 HLA-DPA1 ZBTB7A RNF187 PRDX6-AS1 RNH1 CREM EIF5 DOK2 UBE2S ANKFY1 SNRPN DPY19L1 GYG1 PLP2 TMEM71 C9ORF16 EXOSC1 PPP2R5E ZCCHC2 ERH DOCK11 NDUFS5 NDUFAF8 PRDX6 SPNS3 HSPB1 NOL8 C16ORF91 FUS RBM42 CTD-2201E18-1 LSM7 SDHA HMGXB3 ETS1 EFL1 MARK2 RCOR3 MRPS2 EYA3 TMEM147 BIRC6 MED15 MRPS27 FURIN C12ORF57 ASF1A BUB3 G3BP1 DDA1 SUMO1 PRELID1 PARD6A NTMT1 USP53 CSK NRAS ABL1 TRMT112 ST6GALNAC6 NDUFAF7 SF3B3 TGFB1 WIPI1 TBC1D19 HGH1 PTPN11 ROMO1 ATP5MC1 MYO19 SUDS3 CEP152 RSBN1 HTATIP2 TIMMDC1 LINC00339 SZRD1 PPP1R16B EIF3L ISY1 GYPC DDB1 GNG5 NOSIP HNRNPM IMP4 TOP3A TMEM167A TMEM259 PAF1 UBR5 ERBIN TRAF3 DHX57 PRPF6 RNF126 ADSS2 TRIR TTBK2 AKAP17A ACAA2 SLC25A22 PTPN9 EIF4A1 UBAC1 DYNLL1 PILRB CYBC1 SNHG12 TRAFD1 RBM8A UBAP1 DHX8 XAF1 SYPL1 PLEKHJ1 NDUFB7 MTHFS GPR107 GNB2 OGT HINT2 UBE2E1 P4HTM LENG1 DHX38 MAP4 RSBN1L VMAC NBPF20 IKBKG NCOA1 TSEN15 ICE2 ARL5B TMEM141 IDUA USP9X SLC25A38 CCDC12 ANXA11 SNRPB CXORF38 SUB1 APBA2 RFTN1 SEM1 PHC3 GABARAPL2 MACROH2A1 SEC24B NPTN GNPTAB SIAH2 PEBP1 SNRK PSMD12 DDX18 SH2D2A TMEM9B PABIR2 CYB5B TRAT1 CARHSP1 ZNF609 TIMM22 ATXN7L3B NUP98 NCLN PRKACB WDR83OS SF3A1 MAGOH AGPAT2 EGLN1 SMARCB1 ATP6V1F ZNF138 SOD2 LAMP1 RINT1 AC006369 PTPN2 UCP2 RUNX3 PRDX2 FLI1 UQCRHL DEGS1 ATP5PB H1-2 CAPZB TCF20 UBE2I CEBPA MAP1LC3B2 TAB1 VASP JARID2 DCP2 TPGS1 BTN3A1 LAX1 HSPD1 HEBP2 APPL1 KIF20B TNIP2 TBCB RAD23A UCHL5 MTERF4 HCFC2 PDLIM5 ARMCX3 SLC66A3 TAF10 FOXJ3 PRXL2C TSR3 LTN1 TIMM17B DLG3 NME1 TRGV8 TOMM70 SUN2 TMEM131L CLTB ACAP2 CHERP ZNF622 SRI BECN1 CHMP6 PDPR DNAJC4 RPN1 BRAP SNAP23 RNPC3 ZNHIT1 RP11-47L3 SLC25A19 UBL3 HEIH RCC1L BANF1 DYNLT3 PSIP1 RAB3GAP2 SEL1L TM9SF4 VPS26A TFRC ZNF646 PLAAT3 ZSCAN21 CYLD EIF6 NHLRC2 MTG2 LYAR CUTA STAG2 BOLA3 GLYCTK PARL MRPS16 GK LYSMD3 COG1 PIGT PRR13 ARFGAP3 DRAP1 BTF3 DNAJC1 FAS RC3H2 SEMA7A OCIAD2 EI24 EPG5 NUCB2 ANP32B HNRNPK ATF7IP2 TXNRD2 ZNF829 DUSP28 NUGGC MRPS14 TMUB1 TPRKB LRRN3 ECH1 NUDT4 FLT3LG SET CDC42SE1 HDAC3 SEC11A FOXP1 TOMM22 PLSCR3 EDF1 PHLDA2 ORAI3 ISCU PCED1B-AS1 STK10 YTHDF2 APOL1 KDM2B HELQ USP34 CHURC1 SLC37A1 ARHGEF2 NXT1 MRPL28 CYTOR GNAS PYCARD FRS2 AEN GTF2F1 LEPROTL1 PSMD2 PQBP1 POM121 TTC3 TMUB2 YKT6 ATP1A1 CTDP1 ATP5F1A NDUFB11 LSM5 ZNF207 IQCG AP1S1 FKBP11 HILPDA PPP1R2 EGR2 MZT2A G6PD TMBIM1 NKTR SBNO1 C1QBP BTN2A1 AASDHPPT H1-4 SH3BP5-AS1 NDFIP2 EPAS1 KSR1 FRG1 CCAR1 CHMP1B WDR61 SMPD1 AKNA MRPS15 HLA-F DEF6 ATP11B RPP40 MCM6 CIAO2B RALY RNF135 PDXDC1 TRMT2A PDZD11 BRCA2 MAN1A2 CMTM6 CDK17 MED14 SP1 LRRC23 BRPF1 POMP C1ORF122 RBM6 ZNF614 ACADSB CISD3 ZNF655 KIFAP3 DRG2 YES1 C5ORF24 PAFAH1B3 SF3B5 PLCXD1 NUDT16 ATG2A EMC4 PI4KA STT3B RAB3A TRGV2 WASF2 SLK TMEM14C DARS1 UQCRQ CHORDC1 ATP2A2 LNPK THEMIS AVPI1 NCBP2AS2 URI1 FAM118A NDUFA1 LYSMD2 PCNA TXNL1 MTMR4 SRSF5 MRPL57 ARFIP1 MUS81 CYFIP2 NEU1 SENP1 ECD IER3IP1 CHCHD3 CORO7 HLA-DPB1 HMGN4 MTCH1 MED13L PCSK7 FIP1L1 LARS1 RSL1D1 PTEN MEGF9 PMF1 YWHAZ OSBPL8 JPT1 MTREX ITPR3 DCAKD RNF31 RRM2B PUS7L C11ORF98 TSTD1 MRPL14 ANAPC11 MCRIP1 RNF123 EIF4EBP2 PINLYP IL7R SERGEF CABIN1 VPS29 WIPI2 SUGT1 PUS3 ANKRD54 MSTO1 CREB3 PPP2CA',\n",
       " 'cell_type': 'CD8-positive, alpha-beta memory T cell',\n",
       " 'tissue': 'ileum',\n",
       " 'batch_condition': 'A29',\n",
       " 'organism': 'Homo sapiens',\n",
       " 'sex': 'female'}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_ds_dict[\"train\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['multi_cell_groupings', 'batch_label', 'tissue', 'organism'],\n",
       "    num_rows: 23847\n",
       "})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_multi_cell_arrow_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'multi_cell_groupings': [225, 59, 3, 46, 17],\n",
       " 'batch_label': 'A29',\n",
       " 'tissue': 'ileum',\n",
       " 'organism': 'Homo sapiens'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_multi_cell_arrow_ds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cell 225: A29 ileum\n",
      "Cell 59: A29 ileum\n",
      "Cell 3: A29 ileum\n",
      "Cell 46: A29 ileum\n",
      "Cell 17: A29 ileum\n"
     ]
    }
   ],
   "source": [
    "for cell_idx in train_multi_cell_arrow_ds[0][\"multi_cell_groupings\"]:\n",
    "    print(f\"Cell {cell_idx}:\", split_ds_dict[\"train\"][cell_idx][\"batch_condition\"], split_ds_dict[\"train\"][cell_idx][\"tissue\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the arrow datasets which we need for training: the original cell sentence arrow dataset, and the multi-cell groupings arrow dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-cell Prompt Formatting\n",
    "\n",
    "Here, we will use the C2SMultiCellPromptFormatter class to format a few example multi-cell samples to see what the formatted dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cell2sentence.prompt_formatter.C2SMultiCellPromptFormatter at 0x14b204618fa0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_name = \"tissue_prediction\"\n",
    "prompt_formatter = C2SMultiCellPromptFormatter(\n",
    "    task=task_name,\n",
    "    top_k_genes=100\n",
    ")\n",
    "prompt_formatter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's format a few dataset samples with our custom prompt template to see what the formatted dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['multi_cell_groupings', 'batch_label', 'tissue', 'organism'],\n",
       "    num_rows: 10\n",
       "})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_example_ds = train_multi_cell_arrow_ds.select(range(10))\n",
    "small_example_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'multi_cell_groupings': [225, 59, 3, 46, 17],\n",
       " 'batch_label': 'A29',\n",
       " 'tissue': 'ileum',\n",
       " 'organism': 'Homo sapiens'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_example_ds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['sample_type', 'model_input', 'response'],\n",
       "    num_rows: 10\n",
       "})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formatted_small_example_ds = prompt_formatter.format_hf_ds(\n",
    "    hf_ds=split_ds_dict[\"train\"],\n",
    "    multi_cell_indices_ds=small_example_ds\n",
    ")\n",
    "formatted_small_example_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sample_type': 'tissue_prediction',\n",
       " 'model_input': 'Consider these 100 highly expressed genes, ordered by descending expression, from 5 Homo sapiens cells. Based on the information, your task is to predict the associated tissue type of these cells.\\nCell sentences:\\nCell 1:\\nMALAT1 B2M MT-CO1 TMSB4X HSP90AA1 RPLP1 HSPA1A ACTB HLA-B EEF1A1 MT-CO2 MT-CO3 HSPA8 KLF6 CD69 RPS27 RPS19 CCL5 RPL13 RPS12 NR4A2 DUSP1 MT-ND3 TRAV38-2DV8 JUN RPL34 RPL41 UBC ACTG1 RPS18 RPL28 HLA-C TPT1 RPL32 DNAJB1 RPS15A RPS4X RPL30 ZFP36 RPL35A RPL19 MT-ATP8 RPS21 H3-3B RPS3 RPS29 IFITM1 PFN1 RPS2 MT-CYB RPS28 PPP1R15A JUNB HLA-A IL32 RPS23 RPL7A RPS14 TMSB10 RPS25 RPS15 RPS27A MT-ND1 SRSF7 RPL10 HSP90AB1 FTH1 RPL3 UBB IER2 EIF1 RPS13 RPL11 MT-ATP6 CALM1 RPS8 RPL29 RPS7 RPL39 HLA-E RPLP2 ZFP36L1 MT-ND4L RPL8 FAU NR4A1 TRBV7-6 RPLP0 RPL12 BTG2 CRTAM RPS16 MT-ND4 RPL27A GAPDH RPS6 HSPA1B STK17B RPL37 RPL18A.\\nCell 2:\\nFTH1 DNAJB1 EEF1A1 HSPA1A RPLP1 MALAT1 B2M HSP90AA1 KLF6 RPL10 RPS27 ACTB RPL41 RPL39 RPS12 BTG1 RPL13 PTMA MT-CO3 TMSB4X MT-CO1 RPL28 RPL34 MT-CYB MT-CO2 RPL19 RPS18 RPS2 DUSP1 TPT1 RPS15A RPL26 RPL36 RPS14 RPL30 FOS RPL32 RPS21 RPL11 RPS19 RPS8 RPL3 RPS28 RPS27A VIM RPS3 RPL14 RPL35A JUN UBC HSPA8 HSPA1B MT-ND3 RPS25 RPS4X HLA-C H3-3B RPS23 HSP90AB1 SRGN ZFP36L1 HSPE1 RPLP2 RPS3A RPL37 HLA-B RPL29 MT-ATP6 TNFAIP3 RPLP0 RPL7A MT-ATP8 RPL12 ANXA1 RPL18A MT-ND4L UBB RPS15 FOSB MTRNR2L12 MT-ND1 UBA52 RPS29 ATP5F1E RPS13 RPL9 RPL18 ACTG1 RPS6 RPL17 RPS5 RACK1 ZFP36L2 IL32 EIF1 TSC22D3 RPL8 PNRC1 FAU TUBA1A.\\nCell 3:\\nFTL TMSB4X RPLP1 TMSB10 ACTB FTH1 TPT1 MT1X RPL41 MT1G CD74 RPS18 RPS2 PTMA AIF1 RPS13 RPS3A RPL39 RPS23 TUBA1B SAT1 RPL28 RPS19 RPL13 RPS27A RPS6 RPS8 RPL10 MT2A EEF1A1 GAPDH RPS4X RPLP0 RPS28 RPL26 PFN1 RPL8 SERF2 RPS3 RPL34 RPL37 RPL32 LYZ RPL30 CST3 RPL37A RPL18A RPS12 RPS27 C1QC RPL12 COX8A RPL36 RPS24 B2M RPL21 C1QA MT-CO1 RPL11 RPL18 RPL29 MYL6 RPS15 RPS15A RPL17 H2AZ1 RPL19 RPL35 CFL1 C1QB MT-CO2 ATP5F1E CCL24 RPL36A RPS9 RPL3 ARHGDIB MT-CO3 RPS5 RPL7A RPL10A PPIA RPL15 ARPC1B FAU S100A11 NME2 RPS7 SELENOP HLA-DPB1 RPL23A TYROBP RPS29 UBA52 RPL6 HLA-DRA RAC1 RPL9 GABARAP RPS25.\\nCell 4:\\nMALAT1 CCL4 MT-CO1 CCL5 HSPA1B MT-CYB MT-CO3 MT-CO2 CCL4L2 MT-ATP6 FOSB MT-ND1 ZFP36L2 HSPA1A TMSB4X MT-ND4L UBC MT-ND2 MT-ND5 ATF3 CD69 MYADM JUND JUN PTMA TNFAIP3 ZFP36L1 MT-ND4 HLA-E HSP90AA1 CCL3 RPS18 B2M FOS MTRNR2L12 TRBC2 MT-ND3 PARP14 RPS19 SH3BGRL3 HLA-B DNAJB1 PPP2R5C RPS12 RPS3A DUSP1 ACAP1 HLA-A PNRC1 GAPDH RPL41 HSPA8 S100A6 RPL18A H3-3B UBA52 GABPB1-IT1 ZFP36 PTK2B IRF1 RNF213 RPS15 HELB TAF4B RPS28 NR4A1 ANKRD12 OGA ACTB SEMA4D MBNL1 RPL10A RPL28 MSN YME1L1 DAD1 INTS6 IQCN SIN3B HSPE1 PDCD4 PPP1R15A RPL11 MCL1 MAPRE2 EPG5 INPP5D RALY TPT1 RPL21 GPSM3 RPL13 H1-10 BTG1 RPL17 NACA MIR23AHG BRK1 KLF6 SAMSN1.\\nCell 5:\\nTMSB4X ACTB B2M JUN MALAT1 EEF1A1 MT-CO1 RPLP1 MT-CO2 RPL41 TPT1 KLF6 RPS27 H3-3B HSP90AA1 RPL28 DNAJB1 CCL4 RPL10 RPL13 FTH1 RPS12 MT-CYB HSPA1A RPS18 MT-CO3 HLA-B RPS19 RPL39 MT-ATP8 RPL30 BTG1 RPL11 TMSB10 PTMA RPS28 RPS3 RPL32 HLA-C RPS2 RPS8 RPS15 RPS3A TNFAIP3 MT-ATP6 MT-ND3 SRGN JUNB RPL34 RPS27A NR4A2 RPS15A RPL19 RPS4X RPL26 RPL37 SRSF7 ZFP36 RPSA RPS29 ACTG1 PFN1 HSPA1B RPL3 CCL5 SH3BGRL3 RPL18A ATF3 RPL29 RPS23 GAPDH RPS13 RPL35A RPS25 RPS14 RPLP2 RPL17 MT-ND4L RPLP0 VIM RPL10A RPL12 RPL7A HLA-A ZFP36L1 RPS7 RPS6 RPS5 RPL21 RPS21 RPL8 RPL18 MT-ND1 RPL23A CD52 RPL5 RPL15 RPL37A RPL14 HSPA8.\\n.\\nPredicted tissue:',\n",
       " 'response': 'ileum.'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formatted_small_example_ds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#----Model input:----#\n",
      "Consider these 100 highly expressed genes, ordered by descending expression, from 5 Homo sapiens cells. Based on the information, your task is to predict the associated tissue type of these cells.\n",
      "Cell sentences:\n",
      "Cell 1:\n",
      "MALAT1 B2M MT-CO1 TMSB4X HSP90AA1 RPLP1 HSPA1A ACTB HLA-B EEF1A1 MT-CO2 MT-CO3 HSPA8 KLF6 CD69 RPS27 RPS19 CCL5 RPL13 RPS12 NR4A2 DUSP1 MT-ND3 TRAV38-2DV8 JUN RPL34 RPL41 UBC ACTG1 RPS18 RPL28 HLA-C TPT1 RPL32 DNAJB1 RPS15A RPS4X RPL30 ZFP36 RPL35A RPL19 MT-ATP8 RPS21 H3-3B RPS3 RPS29 IFITM1 PFN1 RPS2 MT-CYB RPS28 PPP1R15A JUNB HLA-A IL32 RPS23 RPL7A RPS14 TMSB10 RPS25 RPS15 RPS27A MT-ND1 SRSF7 RPL10 HSP90AB1 FTH1 RPL3 UBB IER2 EIF1 RPS13 RPL11 MT-ATP6 CALM1 RPS8 RPL29 RPS7 RPL39 HLA-E RPLP2 ZFP36L1 MT-ND4L RPL8 FAU NR4A1 TRBV7-6 RPLP0 RPL12 BTG2 CRTAM RPS16 MT-ND4 RPL27A GAPDH RPS6 HSPA1B STK17B RPL37 RPL18A.\n",
      "Cell 2:\n",
      "FTH1 DNAJB1 EEF1A1 HSPA1A RPLP1 MALAT1 B2M HSP90AA1 KLF6 RPL10 RPS27 ACTB RPL41 RPL39 RPS12 BTG1 RPL13 PTMA MT-CO3 TMSB4X MT-CO1 RPL28 RPL34 MT-CYB MT-CO2 RPL19 RPS18 RPS2 DUSP1 TPT1 RPS15A RPL26 RPL36 RPS14 RPL30 FOS RPL32 RPS21 RPL11 RPS19 RPS8 RPL3 RPS28 RPS27A VIM RPS3 RPL14 RPL35A JUN UBC HSPA8 HSPA1B MT-ND3 RPS25 RPS4X HLA-C H3-3B RPS23 HSP90AB1 SRGN ZFP36L1 HSPE1 RPLP2 RPS3A RPL37 HLA-B RPL29 MT-ATP6 TNFAIP3 RPLP0 RPL7A MT-ATP8 RPL12 ANXA1 RPL18A MT-ND4L UBB RPS15 FOSB MTRNR2L12 MT-ND1 UBA52 RPS29 ATP5F1E RPS13 RPL9 RPL18 ACTG1 RPS6 RPL17 RPS5 RACK1 ZFP36L2 IL32 EIF1 TSC22D3 RPL8 PNRC1 FAU TUBA1A.\n",
      "Cell 3:\n",
      "FTL TMSB4X RPLP1 TMSB10 ACTB FTH1 TPT1 MT1X RPL41 MT1G CD74 RPS18 RPS2 PTMA AIF1 RPS13 RPS3A RPL39 RPS23 TUBA1B SAT1 RPL28 RPS19 RPL13 RPS27A RPS6 RPS8 RPL10 MT2A EEF1A1 GAPDH RPS4X RPLP0 RPS28 RPL26 PFN1 RPL8 SERF2 RPS3 RPL34 RPL37 RPL32 LYZ RPL30 CST3 RPL37A RPL18A RPS12 RPS27 C1QC RPL12 COX8A RPL36 RPS24 B2M RPL21 C1QA MT-CO1 RPL11 RPL18 RPL29 MYL6 RPS15 RPS15A RPL17 H2AZ1 RPL19 RPL35 CFL1 C1QB MT-CO2 ATP5F1E CCL24 RPL36A RPS9 RPL3 ARHGDIB MT-CO3 RPS5 RPL7A RPL10A PPIA RPL15 ARPC1B FAU S100A11 NME2 RPS7 SELENOP HLA-DPB1 RPL23A TYROBP RPS29 UBA52 RPL6 HLA-DRA RAC1 RPL9 GABARAP RPS25.\n",
      "Cell 4:\n",
      "MALAT1 CCL4 MT-CO1 CCL5 HSPA1B MT-CYB MT-CO3 MT-CO2 CCL4L2 MT-ATP6 FOSB MT-ND1 ZFP36L2 HSPA1A TMSB4X MT-ND4L UBC MT-ND2 MT-ND5 ATF3 CD69 MYADM JUND JUN PTMA TNFAIP3 ZFP36L1 MT-ND4 HLA-E HSP90AA1 CCL3 RPS18 B2M FOS MTRNR2L12 TRBC2 MT-ND3 PARP14 RPS19 SH3BGRL3 HLA-B DNAJB1 PPP2R5C RPS12 RPS3A DUSP1 ACAP1 HLA-A PNRC1 GAPDH RPL41 HSPA8 S100A6 RPL18A H3-3B UBA52 GABPB1-IT1 ZFP36 PTK2B IRF1 RNF213 RPS15 HELB TAF4B RPS28 NR4A1 ANKRD12 OGA ACTB SEMA4D MBNL1 RPL10A RPL28 MSN YME1L1 DAD1 INTS6 IQCN SIN3B HSPE1 PDCD4 PPP1R15A RPL11 MCL1 MAPRE2 EPG5 INPP5D RALY TPT1 RPL21 GPSM3 RPL13 H1-10 BTG1 RPL17 NACA MIR23AHG BRK1 KLF6 SAMSN1.\n",
      "Cell 5:\n",
      "TMSB4X ACTB B2M JUN MALAT1 EEF1A1 MT-CO1 RPLP1 MT-CO2 RPL41 TPT1 KLF6 RPS27 H3-3B HSP90AA1 RPL28 DNAJB1 CCL4 RPL10 RPL13 FTH1 RPS12 MT-CYB HSPA1A RPS18 MT-CO3 HLA-B RPS19 RPL39 MT-ATP8 RPL30 BTG1 RPL11 TMSB10 PTMA RPS28 RPS3 RPL32 HLA-C RPS2 RPS8 RPS15 RPS3A TNFAIP3 MT-ATP6 MT-ND3 SRGN JUNB RPL34 RPS27A NR4A2 RPS15A RPL19 RPS4X RPL26 RPL37 SRSF7 ZFP36 RPSA RPS29 ACTG1 PFN1 HSPA1B RPL3 CCL5 SH3BGRL3 RPL18A ATF3 RPL29 RPS23 GAPDH RPS13 RPL35A RPS25 RPS14 RPLP2 RPL17 MT-ND4L RPLP0 VIM RPL10A RPL12 RPL7A HLA-A ZFP36L1 RPS7 RPS6 RPS5 RPL21 RPS21 RPL8 RPL18 MT-ND1 RPL23A CD52 RPL5 RPL15 RPL37A RPL14 HSPA8.\n",
      ".\n",
      "Predicted tissue: \n",
      "\n",
      "#----Response:----#\n",
      "ileum.\n"
     ]
    }
   ],
   "source": [
    "print(\"#----Model input:----#\")\n",
    "print(formatted_small_example_ds[0][\"model_input\"], \"\\n\")\n",
    "print(\"#----Response:----#\")\n",
    "print(formatted_small_example_ds[0][\"response\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model input is formatted with multiple cell sentences, and the response is the tissue type annotation for the multi-cell sample. This is an example of how we can modify the input prompt of Cell2Sentence to encode multiple cells depending on our downstream task.\n",
    "\n",
    "Many prompt variations are possible, and variable number of cells or metadata can be given in input prompts. The only limitation is formatting input prompts accurately depending on the task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now format the full training, validation, and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_formatted_ds = prompt_formatter.format_hf_ds(\n",
    "    hf_ds=split_ds_dict[\"train\"],\n",
    "    multi_cell_indices_ds=train_multi_cell_arrow_ds\n",
    ")\n",
    "val_formatted_ds = prompt_formatter.format_hf_ds(\n",
    "    hf_ds=split_ds_dict[\"validation\"],\n",
    "    multi_cell_indices_ds=val_multi_cell_arrow_ds\n",
    ")\n",
    "test_formatted_ds = prompt_formatter.format_hf_ds(\n",
    "    hf_ds=split_ds_dict[\"test\"],\n",
    "    multi_cell_indices_ds=test_multi_cell_arrow_ds\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll concatenate the formatted training, validation, and test datasets into a single dataset, and make a data split indices dictionary to keep track of which samples belong to which dataset split. The finetune() function will use this data split indices dictionary to split the dataset into train, validation, and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['sample_type', 'model_input', 'response'],\n",
       "    num_rows: 29773\n",
       "})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_formatted_ds = concatenate_datasets([train_formatted_ds, val_formatted_ds, test_formatted_ds])\n",
    "full_formatted_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23847 2948 2978\n",
      "29773\n"
     ]
    }
   ],
   "source": [
    "train_indices = list(range(len(train_formatted_ds)))\n",
    "val_indices = list(range(len(train_formatted_ds), len(train_formatted_ds) + len(val_formatted_ds)))\n",
    "test_indices = list(range(len(train_formatted_ds) + len(val_formatted_ds), len(train_formatted_ds) + len(val_formatted_ds) + len(test_formatted_ds)))\n",
    "print(len(train_indices), len(val_indices), len(test_indices))\n",
    "print(len(train_indices) + len(val_indices) + len(test_indices))  # Check that total number of samples is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "[23847, 23848, 23849, 23850, 23851, 23852, 23853, 23854, 23855, 23856]\n",
      "[26795, 26796, 26797, 26798, 26799, 26800, 26801, 26802, 26803, 26804]\n"
     ]
    }
   ],
   "source": [
    "print(train_indices[:10])\n",
    "print(val_indices[:10])\n",
    "print(test_indices[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_split_indices_dict = {\n",
    "    \"train\": train_indices,\n",
    "    \"val\": val_indices,\n",
    "    \"test\": test_indices,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create CSData object\n",
    "\n",
    "As one last data step, we need to create a CSData object which will wrap around our arrow dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2s_save_dir = \"/home/ap2853/scratch/c2s_api_testing\"  # C2S dataset will be saved into this directory\n",
    "c2s_save_name = \"dominguez_immune_tissue_tutorial8\"  # This will be the name of our C2S dataset on disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bae7e48111b947dfb298433aab73a4b9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/29773 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csdata = cs.CSData.csdata_from_arrow(\n",
    "    arrow_dataset=arrow_ds, \n",
    "    vocabulary=vocabulary,\n",
    "    save_dir=c2s_save_dir,\n",
    "    save_name=c2s_save_name,\n",
    "    dataset_backend=\"arrow\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSData Object; Path=/home/ap2853/scratch/c2s_api_testing/dominguez_immune_tissue_tutorial8, Format=arrow\n"
     ]
    }
   ],
   "source": [
    "print(csdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Cell2Sentence Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will load a C2S model which will finetune on a new dataset, as in tutorial 3. This model can be a LLM pretrained on natural language, or it can be a trained C2S model which will undergo further finetuning on a new dataset of interest. Typically, starting from a pretrained C2S model benefits performance, since C2S models were initialized from natural language-pretrained LLMs and trained on many single-cell datasets on different tasks.\n",
    "\n",
    "For this tutorial, we will start finetuning from the C2S-Pythia-410M cell type prediction model, which was trained to do cell type prediction on many datasets from CellxGene and Human Cell Atlas. More details about the C2S-Pythia-410M cell type prediction model can be found in the Model Zoo section of the ReadME in the GitHub repo, or in the Huggingface model card.\n",
    "\n",
    "We can define our CSModel object with our pretrained cell type prediction model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Define CSModel object\n",
    "model_name_or_path = \"vandijklab/C2S-Pythia-410m-diverse-single-and-multi-cell-tasks\"\n",
    "save_dir = \"/home/ap2853/scratch/c2s_api_testing\"\n",
    "save_name = \"diverse_tasks_pythia_410M_tutorial8\"\n",
    "csmodel = cs.CSModel(\n",
    "    model_name_or_path=model_name_or_path,\n",
    "    save_dir=save_dir,\n",
    "    save_name=save_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSModel Object; Path=/home/ap2853/scratch/c2s_api_testing/diverse_tasks_pythia_410M_tutorial8\n"
     ]
    }
   ],
   "source": [
    "print(csmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finetune on new dataset\n",
    "\n",
    "Now, we will finetune our C2S model on our custom task. The finetune() function will format the full dataset for us using our CustomPromptFormatter instance, so we just need to pass in our custom prompt formatter to the finetune() function.\n",
    "\n",
    "For training, we will need to define training arguments for finetuning our C2S model on our new dataset. Huggingface's Trainer class is used to do training, so we can utilize different training techniques (e.g. mixed precision training, gradient accumulation, gradient checkpointing, etc.) by specifying the corresponding option in the TrainingArguments object. This gives us a vast array of possible options for training, and will allow us to specify important parameters such as batch size, learning rate, and learning rate schedulers. See the full documentation for training arguments at:\n",
    "- https://huggingface.co/docs/transformers/en/main_classes/trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tissue_prediction'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ap2853/scratch/c2s_api_testing/2024-12-08-12_06_53_finetune_tissue_prediction\n"
     ]
    }
   ],
   "source": [
    "datetimestamp = datetime.now().strftime('%Y-%m-%d-%H_%M_%S')\n",
    "output_dir = os.path.join(csmodel.save_dir, datetimestamp + f\"_finetune_{task_name}\")\n",
    "if not os.path.exists(output_dir):\n",
    "    os.mkdir(output_dir)\n",
    "print(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we define our training arguments. For this tutorial, we will use a batch size of 8 with 4 gradient accumulation steps, yielding an effective batch size of 32. We will use a learning rate of 1e-5 with a cosine annealing scheduler, and we will train for 5 epochs total. Some other important parameters specified here are:\n",
    "- bf16: Uses mixed-precision training with bfloat16 dtype\n",
    "- logging_steps: controls how often we log training loss\n",
    "- eval_steps: controls how often we run the eval loop\n",
    "- warmup_ratio: percentage of training in which learning rate warms up to the base learning rate specified\n",
    "\n",
    "Full explanations of all possible training arguments can be found in the Huggingface Trainer documentation: \n",
    "\n",
    "https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.TrainingArguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ap2853/.conda/envs/scFB2/lib/python3.8/site-packages/transformers/training_args.py:1559: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n"
     ]
    }
   ],
   "source": [
    "train_args = TrainingArguments(\n",
    "    bf16=True,\n",
    "    fp16=False,\n",
    "    per_device_train_batch_size=2,\n",
    "    per_device_eval_batch_size=2,\n",
    "    gradient_accumulation_steps=4,\n",
    "    gradient_checkpointing=False,\n",
    "    learning_rate=1e-5,\n",
    "    load_best_model_at_end=True,\n",
    "    logging_steps=50,\n",
    "    logging_strategy=\"steps\",\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    num_train_epochs=5, \n",
    "    eval_steps=50,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    save_steps=100,\n",
    "    save_strategy=\"steps\",\n",
    "    save_total_limit=3,\n",
    "    warmup_ratio=0.05,\n",
    "    output_dir=output_dir,\n",
    "    max_steps=1000  # Set a maximum number of steps - shortened to 1k steps for tutorial purposes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading model from path on disk: /home/ap2853/scratch/c2s_api_testing/diverse_tasks_pythia_410M_tutorial8\n"
     ]
    },
    {
     "data": {
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       "model_id": "aa5bc00be6e64e15995fe1d3cab7b5d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/29773 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/radev/home/ap2853/cell2sentence/src/cell2sentence/csmodel.py:210: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
      "Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training. Output directory: /home/ap2853/scratch/c2s_api_testing/2024-12-08-12_06_53_finetune_tissue_prediction\n",
      "Selecting 500 samples of eval dataset to shorten validation loop.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "max_steps is given, it will override any value given in num_train_epochs\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33map2853\u001b[0m (\u001b[33maakashdp\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.18.5"
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     "data": {
      "text/html": [
       "Run data is saved locally in <code>/gpfs/radev/home/ap2853/cell2sentence/tutorials/wandb/run-20241208_120814-38gshq5r</code>"
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     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/aakashdp/huggingface/runs/38gshq5r' target=\"_blank\">/home/ap2853/scratch/c2s_api_testing/2024-12-08-12_06_53_finetune_tissue_prediction</a></strong> to <a href='https://wandb.ai/aakashdp/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
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      ]
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/aakashdp/huggingface' target=\"_blank\">https://wandb.ai/aakashdp/huggingface</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/aakashdp/huggingface/runs/38gshq5r' target=\"_blank\">https://wandb.ai/aakashdp/huggingface/runs/38gshq5r</a>"
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1000/1000 41:05, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>4.821400</td>\n",
       "      <td>1.194275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>4.758800</td>\n",
       "      <td>1.190113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>4.715400</td>\n",
       "      <td>1.185355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>4.685200</td>\n",
       "      <td>1.181592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>4.711300</td>\n",
       "      <td>1.176304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>4.657900</td>\n",
       "      <td>1.171957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>4.644000</td>\n",
       "      <td>1.171365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>4.644700</td>\n",
       "      <td>1.167198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>4.687100</td>\n",
       "      <td>1.169040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>4.654900</td>\n",
       "      <td>1.161656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>4.631700</td>\n",
       "      <td>1.159571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>4.552600</td>\n",
       "      <td>1.157031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>4.627400</td>\n",
       "      <td>1.153808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>4.580200</td>\n",
       "      <td>1.153421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>4.568700</td>\n",
       "      <td>1.151653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>4.599100</td>\n",
       "      <td>1.149372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>850</td>\n",
       "      <td>4.585900</td>\n",
       "      <td>1.147697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>4.583200</td>\n",
       "      <td>1.146346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>950</td>\n",
       "      <td>4.567900</td>\n",
       "      <td>1.145854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>4.557900</td>\n",
       "      <td>1.145689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning completed. Updated model saved to disk at: /home/ap2853/scratch/c2s_api_testing/2024-12-08-12_06_53_finetune_tissue_prediction\n"
     ]
    }
   ],
   "source": [
    "csmodel.fine_tune(\n",
    "    csdata=csdata,\n",
    "    task=task_name,\n",
    "    train_args=train_args,\n",
    "    loss_on_response_only=False,\n",
    "    top_k_genes=100,\n",
    "    max_eval_samples=500,\n",
    "    prompt_formatter=prompt_formatter,\n",
    "    formatted_hf_ds=full_formatted_ds,  # Pass in formatted dataset, so that formatting is not done again\n",
    "    data_split_indices_dict=data_split_indices_dict,  # Pass in data split indices dictionary\n",
    "    num_proc=1  # Use only 1 process for tokenization\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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